The role of transformative healthcare technology on quality of life during the COVID-19 pandemic

Mohammad Asif Salam (Department of Business Administration, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia)
Saleh Bajaba (Department of Business Administration, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia)

Journal of Enabling Technologies

ISSN: 2398-6263

Article publication date: 19 May 2021

Issue publication date: 28 June 2021

1191

Abstract

Purpose

The purpose of this study is to investigate the role of the COVID-19 health-care system quality (HSQ) and its impact on the individual (satisfaction) and social (quality of life [QOL]) outcomes in the context of a transformative health-care delivery system using service-dominant logic (SDL).

Design/methodology/approach

A sample consisting of 1,008 individuals who have experienced the COVID-19 health-care system was drawn from four different regions of Saudi Arabia using the simple random sampling technique. The survey was conducted using an online survey and 1,008 respondents answered, based on their experience and knowledge of the COVID-19 health-care system. Partial least squares structural equation modeling was applied to test the proposed research model.

Findings

The study findings suggest that service system satisfaction (SAT) significantly mediates the role of the HSQ in delivering and enhancing the QOL. HSQ also has a significant role to play on the SAT as well as the QOL. These findings contribute to the body of knowledge on SDL in the context of HSQ in understanding the significant role of technologies can play in enhancing service satisfaction and better QOL during a crisis such as COVID-19. This study also improves the understanding of the importance of customer-centricity, real-time visibility through tracking and tracing of service flow, agile decision-making, fewer but better-defined service objectives, and finally shaping mindsets and behaviors of all the relevant parties involved in the HSQ service delivery process.

Research limitations/implications

One of the major limitations of this study is that, although COVID-19 is an ongoing global pandemic, cross-sectional data were collected in only one country. The findings may not be generalizable across subsequent waves of the pandemic. The best practices of HSQ could be studied around the globe and the results used to support continuous improvement.

Originality/value

This study advances the understanding of the SDL in the context of a transformative health-care system for a transitional economy by focusing on individual and social well-being during an unexpected crisis such as the COVID-19 pandemic. This study also contributes toward the understanding of the roles of enabling technologies to improve the service delivery system which results in an improved SAT, as well as better QOL for the society at large. Based on SDL this research validates the HSQ model, relevant measures and its overall impact on SAT and QOL in the context of a transformative health-care service system in Saudi Arabia.

Keywords

Citation

Salam, M.A. and Bajaba, S. (2021), "The role of transformative healthcare technology on quality of life during the COVID-19 pandemic", Journal of Enabling Technologies, Vol. 15 No. 2, pp. 87-107. https://doi.org/10.1108/JET-12-2020-0054

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited


1. Introduction

The COVID-19 pandemic is a health and humanitarian crisis, with harsh economic consequences for the overall quality of life (QOL) in communities around the globe (Staples, 2020; IMF, 2020). The pandemic has caused the most severe global recession as the great depression (Gopinath, 2020). COVID-19 has created a health crisis on a scale not experienced in living memory and caused hardship for the entire global health-care system (Diebner et al., 2020). Thousands of health professionals and frontline workers are at risk due to high contact and close contact with the health service delivery process. COVID-19 has a transformative effect on services (Berry et al., 2020) and the quality of service is crucial for an effective outcome. “Transformative service” is a broad term, the central emphasis of which is supporting the well-being of individuals and society at large (Ostrom et al., 2015; Anderson et al., 2013; Newbutt et al., 2020). In delivering health-care service, the ultimate aim is social well-being through better QOL. The health-care service is a rapidly evolving and co-creation process where service providers and patients interact in very close proximity and are at mutual risk (Ostrom et al., 2015), especially during this pandemic. Health-care delivery involves multiple touchpoints, including various stakeholders: patient-to-patient, patient-to-health professionals, health professionals-to-health professionals and-to-documents and surfaces. Each of these contacts can cause infection, sufferings or eventual death and thus this pandemic requires a crisis-inspired quality-driven health-care service and overall QOL for patients and service personnel. It requires a reliable, contactless, technology-enabled, innovative and value-driven health-care system. In that backdrop, the current study examined, what is the role of application-based transformative health system quality (HSQ) during the COVID-19 pandemic and its’ impact on user-satisfaction (service system satisfaction [SAT]) and overall QOL? The answer to this research question contributed to the understanding of transformative HSQ and its impact on SAT and QOL outcomes. In line with our research question, the specific objectives of this study are to: examine the direct relationship between HSQ, SAT and QOL; and, the indirect relationship between HSQ and QOL, mediated by SAT. In this study, service-dominant logic (SDL) was used as a theoretical lens to explain the role of technology in a transformative health-care service system and its underlying relationships. We argue that the concept of holistic quality influences individual (SAT) and social (QOL) outcomes of a service system. The research reported in this paper focuses on the COVID-19 health system in Saudi Arabia. The organization of this paper is as follows. Sections 2 and 3 presents the literature review, conceptual model and hypothesis development. Then, it is followed by methods and findings in Sections 4 and 5. Finally, Section 6 describes the theoretical and practical contributions and provides guidelines for future research.

2. Literature review

2.1 Review of prior models on the application of transformative health-care technology

2.1.1 Information systems success model.

The information systems success (ISS) model has been developed around the focus on integrating information and system quality of specific information technology systems designed to serve a specific purpose such as transformative health care. Earlier studies (DeLone and McLean, 2003, 1992; Halawi et al., 2008; Guimaraes et al., 2009) found the ISS model is one of the most influential theoretical bases to predict and explain the roles of service system use, user satisfaction and information system success. There are six underlying dimensions that define the base ISS model; these are system quality, information quality, systems use, user satisfaction, individual impact and organizational impact. The study by DeLone and McLean (1992) argued that these dimensions are interrelated rather than independent. They also found that both information quality and system quality have an independent and joint impact on use and user satisfaction. Moreover, the rate of usage can cause either a positive or negative impact on user satisfaction. In the study by DeLone and McLean (2003) updated the ISS model by incorporating the “service quality” measures that resulted in a revised model comprised of a set of dimensions, for example, information quality, system quality, service quality, use/intention to use, user satisfaction and net benefits. The authors also found that the Web-based application processes fit well into the updated ISS model and the six success dimensions. One potential criticism for the ISS model is premised on the assumption of voluntary use which can lead to inconsistent findings (Chen et al., 2002), which might be caused due to the reliance on a single theoretical premise that excludes consideration of other possible determinants. Hence, this study SDL is a viable theory to consider for alternative theoretical evidence.

2.1.2 Service-dominant logic and COVID-19 health-care system quality.

The underlying argument of SDL is based on service exchange – the deployment of resources to deliver benefits for another entity (Vargo, 2018; Vargo and Lusch, 2017). SDL focuses on service-oriented relationships between providers and recipients, predominantly customer-focused and supporting the notion of value-in-use (Vargo and Lusch, 2017). The main proposition of SDL is that a service is a basis for exchange and all social and economic actors are part of the service, as resource integrators, value co-creators. Value is judged by the service recipient (Vargo and Lusch, 2018, 2016). These central tenets of SDL are important for the concept of quality in service systems (Lusch and Nambisan, 2015). Maglio and Spohrer (2013) defined the service system as a dynamic value co-creating mechanism of resources, which is a complex and dynamic network structure consisting of people, technology, organizations and shared information. Table 1 links the service quality (SERVQUAL) dimensions to COVID-19 HSQ, interaction quality and information quality, with the relevant literature.

The service system is an integrated whole that influences satisfaction and value creation (Edvardsson et al., 2011). The service system relies on continuous interactions, networks of relationships and value co-creation. The viability of service systems requires the transformation of socio-economic relationships, a critical component of value co-creation in SDL (Vargo et al., 2008). Lusch et al. (2010) and Parasuraman and Grewal (2000) argue that service systems’ viability is dependent on service excellence and closely related to the quality-value-loyalty chain. Karpen et al. (2015) emphasized developing superior service systems for value co-creation by integrating resources through SDL. SDL conceptualizes COVID-19 HSQ as a service system and its impact on SAT and QOL (Anderson and Ostrom, 2015).

2.2 Related studies on the use of technologies to address the COVID-19 pandemic

Several recent studies have addressed the roles of technological enablers in responding and managing the COVID-19 pandemic. This pandemic has implications for the future design, development and use of technologies to deliver safe and contactless health-care services (Sein, 2020; Bardhan et al., 2020; Park and Humphry, 2019; Venkatesh, 2020; Wosik et al., 2020). In a related study, He et al. (2021) found that the technologies can be useful to mitigate the sufferings on human lives, organizations and the society at large. Brohi et al. (2020) found disruptive technologies powered by artificial intelligence (AI), machine learning, image recognition and deep learning algorithms can be used for prevention, early detection, diagnosis of the infection and faster drug and vaccines development for effective health-care system management. Repurposed existing technologies, for example, AI, three-dimensional printing technology can help make face masks and other personal protective equipment and assist in social distancing enforcement and contract tracing (Sipior, 2020; Markforged, 2020). Big data analytics has been found to be successfully applied to help identify COVID-19 cases, vaccine development and remote real-time alert generation through analyzing clinical visits, travel history and clinical symptoms (Wang et al., 2020a; Wang et al., 2020b; Watson et al., 2020).

On a similar stream of study, Jazieh and Kozlakidis (2020) argued remote care or telehealth technologies can facilitate an added flexibility to the responsiveness of the health-care system in times of crisis. Anthony (2021a, 2021b) discussed the benefits of integrating telemedicine for digital health care management to facilitate digital health care to administer remotely for the management of the COVID-19 pandemic. On the other hand, Sinsky and Linzer (2020) suggested health-care systems should take advantage of the unique lessons from the COVID-19 to transform for better care for individuals, better health for the population, better experience for clinicians and efficient service. Bokolo (2021) highlighted the role of the adoption of telehealth and digital care solutions during the pandemic that can benefit society. Additionally, Scott (2020) emphasized the use of digital technologies and data analytics-enabled AI-based algorithms in a responsible manner for the protection of user-related data to incentivize users. Anthony (2021a, 2021b) studied the application of telemedicine and e-Health as a proactive measure to improve clinical care to manage the COVID-19 pandemic as a convenient, safe, scalable, effective and sustainable method of providing clinical care. Finally, Marr (2020) found, use of mobile apps via smartphones and video-conferencing tools can be effective to track the movements of individuals, alert people from visiting COVID-19 hotspots, help doctors to diagnose patients through video services and telemedicine, support communities with online shopping, e-learning, online meetings and telework. Based on this discussion, it is evident that technology played a crucial role in managing COVID-19 the current study focused which is also the focus of current study to analyze the impact of such technologies on user satisfaction and overall QOL.

3. Research model and hypotheses

There are three main components of COVID-19 HSQ that influence service quality perceptions and they are system quality, interaction quality and information quality. These are described in the following paragraphs.

3.1 Systems quality

COVID-19 HSQ reflects users’ perceptions of communication’s technical level (Petter and McLean, 2009). There are four underlying determinants of systems quality: system reliability, system efficiency, system flexibility and system privacy. System reliability is the extent to which the system is dependable over time (Nelson et al., 2005) and it measures service promise. System efficiency is the extent to which the service system is easy to use (Parasuraman et al., 2005). System flexibility is the extent to which a system can be adapted to various user needs and changing conditions (Nelson et al., 2005; Parasuraman et al., 2005). System privacy measures the extent to which the system protects the privacy of patients’ health information (Parasuraman et al., 2005). In health care, privacy is always an important parameter. Therefore, the present study uses these four aspects as the important aspects of the COVID-19 HSQ measure.

3.2 Interaction quality

Interaction quality is the quality of interpersonal interaction and the interplay between the COVID-19 health-care service system and each user (Dagger et al., 2007). It is defined over a period of time during which the consumer interacts directly with the service (Bitner, 1990). It is assessed in terms of the provider’s knowledge and competence, promptness in providing solutions and individual attention. These three core facets are also described as responsiveness, assurance and empathy. The first aspect, responsiveness, refers to the service provider’s willingness to help users and deliver prompt service (Parasuraman et al., 1988; Sousa and Voss, 2006). The second one, assurance, measures the perceived safety of the COVID-19 health-care system (Parasuraman et al., 1988; Sousa and Voss, 2006). Safety is critical in generating patient trust and confidence. The third one, empathy, measures the perceived caring and individualized attention of the health-care service provider for patients (Parasuraman et al., 1988; Sousa and Voss, 2006). This study considers these three aspects as indicators of interaction quality in COVID-19 health-care service systems and an essential part of the HSQ measure.

3.3 Information quality

Information quality is a critical facet of the COVID-19 health-care service system. It is what a user (or patient) receives due to his or her interactions with a COVID-19 health-care service provider. The literature highlights the importance of perceived information quality and it is linked to several service benefits, which may vary in importance for the user (Nelson et al., 2005). The health-care literature supports the idea that there is a direct relationship between information quality and service quality (Xu et al., 2013). There are two underlying aspects of information quality. First, utilitarian information refers to how well the COVID-19 service system fits its actual purpose. The second, hedonic information refers to the extent to which the COVID-19 service system generates positive feelings. A concern for the hedonic benefit has directed attention to users’ beliefs about service quality (Sweeney and Soutar, 2001). We consider information quality as an essential aspect of the COVID-19 service system, encompassing utilitarian and hedonic characteristics.

3.4 The COVID-19 health-care system quality

Based on the supporting literature, the COVID-19 health-care system quality is proposed in Figure 1, which indicates the underlying dimensions of COVID-19 HSQ. It is comprised of three primary facets (system quality, interaction quality and information quality) and nine components (system reliability, system efficiency, system flexibility, system privacy, responsiveness, assurance, empathy, utilitarian information and hedonic information). All of which represent the overall measure of COVID-19 HSQ.

3.5 Effects of quality in COVID-19 health-care system quality

A quality system substantially improves the health-care system through differentiation and serving large numbers of patients (Akter et al., 2019). The outcomes of COVID-19 HSQ are presented in Figure 2. This model represents the impact of HSQ on the individual (satisfaction) and social (QOL) levels. Following SDL, the model explains service system challenges and opportunities with a cognitive (HSQ), affective (SAT) and connotative (QOL) framework (Maglio et al., 2015; Huang and Rust, 2013; Dagger et al., 2007; Oliver, 1999; Oliver et al., 1997).

3.6 The effects of COVID-19 health-care system quality on service system satisfaction and quality of life

Satisfaction is a significant indicator of performance (Nelson et al., 2005). The health-care literature suggests that satisfaction should be modeled individually and linked with overall service quality to measure service performance (Dagger et al., 2007). In the health-care literature, service quality plays a crucial role as a tool to ensure patient satisfaction (Wixom and Todd, 2005). In health care, the SAT is an important indicator of quality and can lead to improvements in patient retention and profitability (Säilä et al., 2008). SAT is a vital and integral component of the health service system’s strategic processes (Choi et al., 2004) and should receive the same attention as HSQ to design and manage the COVID-19 health-care systems effectively. The literature identifies SAT as an effective measure of cognitive service quality. This suggests a causal relationship between HSQ and SAT with COVID-19 HSQ as an antecedent of SAT (Dagger et al., 2007). Thus, the first hypothesis is:

H1.

Perceived COVID-19 HSQ positively influences SAT with the service system.

The relationship between HSQ, SAT and perceptions of QOL are well-established in the quality literature (Dagger and Sweeney, 2006). Lusch et al. (2007) argued that the customer is a primary integrator of resources in creating value through service experiences that are intertwined with life experiences to enhance life quality (Payne et al., 2008). QOL is described as the well-being and happiness of individuals (Yuan, 2001). QOL is a subjective concept (Sirgy et al., 2006), which is often used interchangeably with well-being (Yuan, 2001). Broadly, QOL can be conceptualized as an overall measure or as a measure based on experiences in various domains, such as health care, work, family and leisure (Lee et al., 2002). Holistically, QOL refers to the subjective evaluation of one’s current life circumstances (Dagger and Sweeney, 2006). However, in the health-care context, QOL is viewed as a subjective, individual, experiential construct that measures overall well-being in that particular context (Dagger and Sweeney, 2006). In related disciplines, the relationship between HSQ, SAT and QOL has been explored to evaluate the performance of a service (Sirgy and Cornwell, 2001). Thus, the second and third hypotheses are:

H2.

SAT with a service system positively influences perceived QOL.

H3.

Perceived COVID-19 HSQ positively influences the perception of QOL.

3.6.1 Mediating effects of satisfaction.

Studies have discussed an indirect relationship between service quality and social outcomes through satisfaction (Choi et al., 2007). It is important to understand how SAT mediates the relationship between HSQ and QOL (i.e. a social outcome). Despite a natural connection between HSQ, SAT and QOL, few studies have assessed this relationship. Thus, the fourth hypothesis is:

H4.

SAT mediates the relationship between COVID-19 HSQ and QOL.

The conceptual model of this study is presented in Figure 2.

4. Research method

Saudi Arabia is one of the leading emerging economies in the Middle East and a key player within the Gulf Cooperation Council, at the same time Saudi Arabia is playing a major role as a member of the G20 economies. The country is currently going through an economic and social transition under the policy known as the “Vision 2030,” which has been implemented since 2017. As a part of this on-going change in many spheres of the Saudi Arabian economy, health care is one of the vital sectors that are going to be reformed to be customer/patient-centric. During this transition and call for reform, COVID-19 emerged as a testing ground for the efficiency of the health-care system which was predominantly managed as a public-funded system. Hence, this study is going to offer valuable implications for transformative health-care systems in transitional economies, especially during a crisis such as COVID-19 to successfully manage their HSQ, which is going to contribute toward creating SAT for the patients and ensure a better QOL. This study aims to understand how a technology-enabled transformative health-care system can deliver social well-being and enhance the QOL in the context of a developing country such as Saudi Arabia during the COVID-19 pandemic. At the onset of the COVID-19 pandemic in the year 2020, the governments’ public health ministry has launched several tracking applications i.e. “Sehatty” (hotline for help and appointments), “Tabaud” (warning and information about the surrounding infections), “Tawakkalna” (tracing records and monitoring system for prevention) to help contain the spread of the virus and ensure monitoring public health safety measures, currently these applications are critical portals for managing access to public facilities, including the administration of COIVD-19 vaccines. These transformative app-based health-care systems are aimed at connecting the patients with the health service providers and serving the community with trusted universal health-care service. The primary health service application portal is known as “Tawakkalna,” which was launched by the Ministry of Public Health of Saudi Arabia is an integrated transformative health service platform that has been launched after the COVID-19 pandemic broke-out and became a pivotal instrument to manage the COVID-19 health-care system across communities. The participants in this study were patients and they went through the interactions with the application and its’ subsequent services.

4.1 Measurement scales

In line with this study’s objective, a set of multi-item scales were adopted from the literature to measure the variables of interest. Scales were adapted to the context of COVID-19 health service systems where necessary. Table 2 provides the constructs and their attributes, as well as sources for the scales. Seven-point Likert scales were used to measure all the constructs except satisfaction. A bipolar semantic differential scale ranging from “very dissatisfied” to “very satisfied” was adopted to measure satisfaction. The survey was initially composed in English and then translated into the local language, Arabic. The Arabic version was then translated back into English. A panel of experts fluent in English and Arabic verified that both versions reflected the same content (Andaleeb, 2001). A pretest of the Arabic version of the survey was conducted with a convenience sample of 30 participants and confirmed that the question wording, format, sequence, length, range of scales and instructions were appropriate. Based on the pretest results, some revisions were made.

4.2 Participants and procedures

All the participants of the survey were COVID-19 patients who had first-hand experience in availing the health-care services through the app-based system and residing within Saudi Arabia. This health service app was designed to serve both patients mainly and later on non-patients, as well for tracking and tracing. During data collection in line with the objectives of this study only the patients who had first-hand the experience were included in this survey. A simple random sampling was used to collect data from four Saudi Arabia regions, i.e. eastern, western, northern and southern, which ensured that each sample/element had an equal chance of being selected. The measurement unit was COVID-19 patients who had recently experienced the health-care service due to the COVID-19 exposure during the peak periods of COVID-19 between March and September of 2020. Participants were selected based on their interaction with the health service applications. The participants were shown evidence of ethical approval and assured of their anonymity. Of 1,072 returned questionnaires, some were omitted because they were incomplete, leaving 1,008 usable responses after the list-wise deletion procedure was outlined by Hair et al. (2018). The final sample consisted of 58% men and 42% women respondents. In total, 60% of respondents have higher education degrees and 38% are married. Despite the satisfactory response rate, non-response bias was checked by comparing the survey respondents’ profiles and those on the sample frame using demographic variables and no non-response bias was found using χ2 tests (Kim et al., 2012). Similarly, no significant response bias was found after comparing the early (20%) and late (20%) response groups on the survey items using paired t-tests. Table 3 summarizes the demographic information about the sample.

There are demographic implications of this study that emerged based on the samples represented in this study. A vast majority of the survey participants were relatively young to middle-aged and their monthly income falls in the middle-class category. This representation of samples suggests there is a need for more preventive measures to put in place at the community level to reduce the number of infections. HSQ needs to focus on raising community-level awareness about the social, as well as physical distancing to reduce infections and growing pressure on the health-care system due to the community-level person-to-person spread of the COVID-19 pandemic. This will enable to lessen economic and social burden.

4.3 Common method bias

As all measures were self-reported, the impact of common method bias (CMB) should be analyzed. Established recommendations were followed to ensure that CMB is eliminated or minimized (Podsakoff et al., 2003). According to Kock (2015), the occurrence of a variance inflation factor (VIF) greater than 3.3 is proposed as an indication of pathological collinearity and indicates that CMB may contaminate the model. The results of the current study show that the VIF scores range from 1.32 to 2.54. Therefore, CMB was not considered to be influential (Kock, 2015).

4.4 Statistical procedures

The current study used a cross-sectional survey design. We ran the preliminary analysis, including descriptive analysis, using the statistical package for social science software (SPSS, Version 27). To test our hypotheses, we used partial least squares path modeling (PLS-PM) (Version 3.3.2). This method is suitable for testing the mediation effects due to its capacity to test for indirect effects with various options and it allows researchers to test relationships among variables simultaneously. Specifically, this study uses consistent partial least squares (CPLS) (Ringle et al., 2015) to mimic the covariance-based structural equation modeling (SEM) in testing or confirming the theory (Dijkstra and Henseler, 2015). CPLS provides a correction for estimates when PLS is applied to reflective constructs and allows for the adjustment of the original estimates to accommodate common factor models (Kock, 2019). For CPLS, the required sample size should be large and no fewer than 100 cases (Kock and Hadaya, 2018). The number of iterations on the CPLS is 300 (Hair et al., 2017). The significance of the path analysis, t-scores, p-values and corresponding 95% bias-correlated and accelerated bootstrap confidence intervals were obtained by choosing a bootstrapping procedure (with a sub-sample of 5,000, using no sign changes) and 5% significance. PLS-PM is a popular tool for analyzing complex relationships (Sarstedt and Cheah, 2019). In most management studies, CPLS has been used as a tool for analysis (Latan et al., 2018). Unlike other multivariate techniques, PLS-PM does not depend on the assumption of normality because it is non-parametric. Multicollinearity and goodness of fit indices are considered for model assessment. Overall, the data analysis for hypothesis testing in the current study consists of four stages. First, we assess the measurement model to ensure that each construct is reliable and valid (Bandalos, 2018; Furr, 2017). Second, we assess the structural model in terms of model suitability for the observed data (Aguinis et al., 2018; Hair et al., 2018). Third, we analyze the direct effects between each predictor variable and its outcome to establish the nature of the relationship (Pierce and Aguinis, 2013). Finally, we examine the hypothesized predictor variables’ indirect effects and their outcomes when mediators are involved (Hayes, 2013).

5. Data analysis and results

Based on the results from the PLS-SEM Table 4 summarizes the item loadings and Table 5 presents the means, standard deviations, correlations, reliability and validity estimates of the study variables. Service system quality was found to be positively correlated with SAT and QOL (r = 0.73, r = 0.65, p < 0.01), providing initial support for H1 and H3. Furthermore, SAT was also found to be positively correlated with QOL (r = 0.61, p <* 0.01), providing initial support for H2.

5.1 Measurement model

The present study used PLS-based SEM (SmartPLS) to analyze the data and test the hypotheses of this study (Hair et al., 2017). This method provides a comprehensive understanding of the whole study by incorporating the measurement model and structural model simultaneously (Hair et al., 2017). This study examined the measurement model by evaluating the confirmatory factor analysis item loadings, reliability and validity reports. Table 4 shows the item loadings and all the items were highly loaded on their respective constructs, indicating that they converge on their respective constructs (Hair et al., 2018). Table 5 shows the reliability of the measured constructs. Cronbach’s α ranges from 0.78 to 0.94, which is above the minimum acceptable cutoff (Hair et al., 2018).

Regarding convergent validity, the result shows that the average variance extracted (AVE) of the constructs ranges from 0.37 to 0.56. SAT is above the minimum acceptable limit of 0.50, but service system quality and QOL are not (Hair et al., 2018; Hair et al., 2017). However, the composite reliability (CR) for the constructs was high enough to offset this limitation. Finally, regarding discriminant validity, the results indicate that the square roots of the constructs’ AVEs are higher than their correlations with the other variables, which means that there is discriminant validity among the constructs (Fornell and Larcker, 1981). Also, the Heterotrait-Monotrait ratios, above the diagonal in Table 5, are within the acceptable range (Hair et al., 2018). Thus, both convergent and discriminant validity were supported.

5.2 Structural model

The structural model was tested by investigating β, p-value, R2, f2 and Q2 estimates. β measures the strength of the relationship between the observed variables and R2 explains the overall predictability of the structural model (Hair et al., 2018). The p-value signifies the level of significance to determine whether a hypothesis is supported or not. In addition to looking at changes in R2, f2 indicates the meaningfulness of the effect size of each predictor. In SmartPLS, the blindfolding procedure helps to generate values of Q2, which applies a sample re-use technique that omits part of the data matrix and uses the model estimates to predict the omitted part. Studies recommend values of β above 0.20 (Hair et al., 2018) and R2 and f2 with values above 0.13 and 0.15, respectively (Cohen, 1977; Hair et al., 2018). It is also recommended to have a value of Q2 higher than zero to indicate predictive relevance. Table 6 displays the β, p-values, R2, f2 and Q2 of the hypothesized structural model. The β and R2 values were found to be above the minimum threshold and the p-values showed that all the path relations are significant. As for the f2, all the paths showed moderate effects (f2 between 0.12 and 2.19). Table 6 shows the Q2 values of the dependent constructs: Q2(service system satisfaction) = 0.36 and Q2(quality of life) = 0.28. All the Q2 values were above zero providing support for the conceptual model’s predictive relevance in this sample (Hair et al., 2012). Overall, the R2, f2 and Q2 values indicate a reasonably strong explanatory power. Regarding the model’s goodness of fit, the standardized root mean square residual had a value 0.07, indicating no discrepancy between the implied model and the observed one (Hair et al., 2018).

5.3 Hypothesis testing

Table 6 reports the exogenous variables’ direct effects on the endogenous variables, the coefficient parameters and the associated 95% bias-corrected confidence intervals. The estimates show that the direct effect of service system quality on SAT was significant (β = 0.83, p < 0.00) and the direct effect of SAT on QOL was also positive and significant (β = 0.37, p <* 0.00), Also, the direct effects of service system quality on QOL was positive and significant at (β = 0.46, p < 0.00). These findings provide support to H1, H2 and H3. Following Blanco-Oliver et al. (2018), we also examined the variance accounted for (VAF) for the mediated relationship to assess the indirect effect’s magnitude in relation to the total effect. This is a test for whether mediation exits in the relationship. VAF computes the proportion of a dependent variable’s variance explained by an independent variable indirectly via the mediator(s). According to the results, mediation exists in the hypothesized relationship (H4) as the VAF is equal to 0.40. Table 6 shows that SAT mediates the relationship between service system quality and QOL, where the indirect effect is significant (β = 0.31, p <* 0.00), supporting H4.

6. Discussion

6.1 Research implications

This study investigated the links between COVID-19 HSQ and SAT and the overall QOL for society at large in the context of a transformative health-care delivery system in Saudi Arabia, using SDL. COVID-19 HSQ was conceptualized as a measurement comprised of items related to service quality, interaction quality and information quality. It was found that to address any health-care service challenge, an emphasis on identification and resolution of quality issues is of utmost importance. The findings also indicate that the visualization of a systemic process and problem solution starts with putting the customer at the center. In this study, SAT serves as the central link between HSQ and QOL and HSQ has a significant direct effect on QOL. System quality, interaction quality and information quality are fundamentals that can transform the quality of the entire health-care system. All these three quality drivers eventually impact SAT and overall QOL in the context of the COVID-19 pandemic. There are several findings that emerged from the study which can improve the HSQ. Among these two are worth mentioning. First, among the three underlying dimensions representing the HSQ, for example, system quality (AVE = 0.43), interaction quality (AVE = 0.40) and information quality (AVE = 0.52), the AVE by the HSQ measurement model, information quality appears to be the strongest dimension. This implies sharing and communicating timely and accurate information is crucial to building trust in the HSQ. Second, from the survey data it is also apparent that utilitarian information that serves COVID-19’s preventive measures and hedonic information that arouses positive feelings through alleviating concerns/worries are more important. This is also suggesting that the participants of the study would prefer to be aware of COVID-19 related information well in advance so that they will be able to take precautionary measures to avoid infections.

In Saudi Arabia, as in the rest of the world, the COVID-19 pandemic has forced health-care service providers to be resilient, efficient and more responsive. The heightened importance of health-care service systems during the COVID-19 outbreak focuses on service quality and the crucial connections between patients and health-care service providers. Building on SERVQUAL and SDL (Vargo, 2018; Vargo and Lusch, 2017), this study developed and validated a quality model and measured the overall impact of quality on an individual (satisfaction) and social (QOL) outcomes in the transformative health-care delivery system of Saudi Arabia. The conceptual model was based on three attitudinal foundations: cognitive (service quality), affective (satisfaction) and connotative (QOL). It identified and combined three primary aspects of the health service system: system quality, information quality and interaction quality. The study advances theory and practice in service quality systems research by focusing on individual and social well-being during a pandemic such as COVID-19. The health-care system’s ultimate aim is patient and social well-being and the system emphasizes the well-being of other service entities, including employees and the community, through preventive measures. Berry et al. (2020) argued that health-care services are founded on the concept of inseparability, where patients must be physically present to receive care. However, the emergence of the internet and technological advancement has led to the first wave of health-care service separability in the forms of telemedicine and remote health-care intervention through telehealth, digital therapeutics and care navigation. Concern over COVID-19 is now powering the next wave of biomedical and health-care technology in which traditionally inseparable health services are increasingly being offered fully or partially in isolation. This clearly demonstrates that a combination of clinical care and technologically advanced health service delivery will play a key role in enhancing QOL for all stakeholders. Based on the data analysis, HSQ has a direct effect on the QOL of members of the community. Based on SDL, this implies that HSQ is a resource integrator of medical facilities, such as doctors, nurses, medicine, pharmaceutical staff, equipment and other relevant agencies. This process of resource integration is crucial for the QOL of the community at large. The research findings demonstrate that SAT mediates the relationship between HSQ and QOL. Satisfactory service exchange delivers individual well-being through the deployment of related resources, which subsequently results in an improvement of the overall QOL of members in the community. Using SDL as the theoretical lens, the current study demonstrates the role of HSQ in individual service satisfaction and social outcomes measured as QOL. Despite growing interest in service quality research, the social impact of services on QOL has not been made clear (Ostrom et al., 2015). This study uses SDL to examine the direct and indirect role of HSQ in improving individual service satisfaction and social QOL in the context of the COVID-19 pandemic.

The study findings indicate that COVID-19 has further complicated the situation with structural shifts, which create both risks and opportunities for disruptive innovation. As Staples (2020) found, national lockdowns’ stringency is not well-correlated with changes in gross domestic product. Moreover, successful control of the virus is the key to unlocking the economy by restoring consumer confidence to re-engage in economic activity, which will eventually enhance productivity. Strict measures to control COVID-19 have enabled a return to normalcy in Saudi Arabia. This indicates that controlling the virus permits a return to economic stability by stimulating consumer demand. This pandemic has tremendous implications for a shift to a new set of social expectations or new normal, which may change the consumption behavior and people’s priorities in life. In practice, the findings suggest that quality improvements can be brought about by focusing on reliability, availability, efficiency, privacy, responsiveness, assurance, empathy, utilitarian information and hedonic information. For example, system quality may be improved by enhancing reliability, efficiency, availability and privacy. Interaction quality may be increased by enriching customer experience with commitment and dedication. Information quality can be enhanced by delivering utilitarian and hedonic benefits, such as easy access to the service system, keeping promises and a supportive attitude. The research model may be used as a service system analytical tool to link service dimensions with their outcomes. All these dimensions and sub-dimensions of HSQ should not be treated in isolation but should be integrated into a holistic service delivery system.

6.2 Practical implications

The COVID-19 outbreak intensified commitment to global, as well as, local public health preparedness through proactive planning to respond to emergencies. The lessons learned of the gap in existing health-care technology and their capacity to deal with epidemics is a precious lesson to enable the transformation of future health-care systems. In addition, technologically empowered solutions need to have a benchmark for the greater integration of such technologies as part of routine health-care design and provision. Based on anecdotal evidence, it has been found that the adoption of digital contact tracing apps, for example, Tawakkalna is mandatory in Saudi Arabia. It has been recognized that the health-care operators committed a substantial amount of resources for quick adoption and disseminated these technologies for mass utilization with an objective to contain the spread of COVID-19 in public and commercial facilities. Study findings suggest Saudi Arabia initiated policies to legislate technology-led transformative health-care service during the COVID-19 pandemic in the national health-care system, this is the time to initiate necessary policy changes that supports wide adoption of transformative health services to help reduce the spread of COVID-19. Respectively, findings from this study provided a description on how health practitioners, patients and the entire medical centers are deploying app-based and other related digital services. Also, this study recommends further systematic review on the application of information systems-driven medical solutions as a powerful tool to provide seamless communication while preserving the safety of self-quarantine patients within the current pandemic. Also, it is evident that such technologies are well-suited for the COVID-19 pandemic and may not be able to provide solutions to all types of medical challenges. COVID-19 has driven society into a remote service system, hence, it is expected that digital care is here to stay.

6.3 Research limitations and future research

COVID-19 has disrupted many aspects of our lives, including employment prospects, economic prosperity, education, business, social life in the form of social distancing, personal and professional relationships. People’s health, financial well-being, socio-economic life and day-to-day normalcy are suddenly at risk and uncertain. This pandemic’s dynamism has imposed several limitations on this study, which may be addressed by future research. First, the current study was conducted at one point in time and within one country, even though this is an on-going global crisis. The findings are not generalizable to other countries. To add validity to the model, it should be replicated in other countries and other settings. Second, under the influence of the COVID-19 pandemic, the study samples were drawn from the population of a developing economy, again limiting the findings’ generalizability. Moreover, cultural differences might play a role in the perception of system quality causes and consequences in different countries. Different psychological attitudes are prevalent, such as individualism/collectivist, risk-taking/risk-avoidance, high power distance/low power distance and masculinity/femininity. There is also a need to understand the various aspects of QOL that are threatened as a consequence of the pandemic, such as personal, psychological, mental, social and relational.

7. Conclusion

The current study analyzed the impact of HSQ on QOL from a social standpoint using SDL as a theoretical lens. This pandemic is an overarching crisis of a lifetime, affecting the QOL on multiple fronts. It started as a health-related issue, but eventually, it turned out to be a socio-economic challenge over a matter of months. The current study empirically tested and validated the link between health-care service quality, service satisfaction and QOL in the context of the transformative impact of COVID-19 on a service system. The results of this study support the role of system quality and its impact on individual and social outcomes and QOL. Also, the findings suggest that there is a direct impact of system quality on QOL and an indirect effect on patients’ satisfaction with the service system. This implies that the health-care service system should be designed, developed and deployed based on user-defined expectations to deliver its full potential.

Figures

COVID-19 health-care system quality (HSQ)

Figure 1

COVID-19 health-care system quality (HSQ)

Conceptual model of COVID-19 health system quality

Figure 2

Conceptual model of COVID-19 health system quality

Constructs linked to service quality

Constructs linked to service quality Outcome constructs Sources
SERVQUAL dimensions (i.e. reliability, responsiveness, assurance and empathy) User satisfaction, job performance Parasuraman et al. (1988); Pitt et al. (1997, 1995)
SERVQUAL dimensions
System quality, information quality, service quality
Intention to use, use, user satisfaction, net benefits
Information satisfaction, system satisfaction, information*system satisfaction
Jiang et al. (2000); and
Akter et al. (2019)
System quality (reliability, flexibility, integration, accessibility and timeliness) and information quality (completeness, accuracy, format, currency). Information satisfaction, system satisfaction, usefulness, ease of use, attitude and intentions Nelson et al. (2005)
Similar dimensions proposed by Nelson et al., Perceived value, loyalty intentions Wixom and Todd (2005); (Nelson et al., 2005)
Core dimension: systems efficiency, systems availability, fulfillment and privacy. Recovery dimensions: responsiveness, compensation and contact Overall customer satisfaction Parasuraman et al. (2005)
Environment quality (graphic quality and clarity of layout), delivery quality (attractiveness of selection, information, ease of use, technical quality) and outcome quality (reliability, functional benefit and emotional benefits). Information satisfaction, system satisfaction, service satisfaction, usefulness, ease of use, enjoyment, attitude, intention. Fassnacht and Koese (2006)
Information quality (completeness, accuracy, format and currency), system quality (reliability, flexibility, accessibility and timeliness), service quality (tangibles, responsiveness, empathy, service reliability, assurance) Service satisfaction, perceived ease of use, perceived usefulness Xu et al. (2013)

Construct operationalization

Constructs Sub-constructs Definitions Studies
Systems quality Systems reliability
Systems flexibility
Systems efficiency
Systems privacy
The degree to which COVID-19 health-care system is dependable over time
The degree to which COVID-19 health-care system is adaptable to meet variety of patient needs
The degree to which COVID-19 health-care system is easy to use and adapt to a variety patient needs and changing conditions
The degree to which COVID-19 health-care system is safe and protects patient information
(Nelson et al., 2005)
(Nelson et al., 2005)
(Parasuraman et al., 2005)
(Parasuraman et al., 2005)
Interaction quality Responsiveness
Assurance
Empathy
It refers to the willingness of physicians to help patients and provide prompt service over COVID-19 health-care system
It measures knowledge of the health-care service provider to inspire trust and confidence of patients
It measures caring and individualized attention of the health-care service provider to its patients
(Parasuraman et al., 1988)
Information quality Utilitarian
Hedonic
The extent to which the COVID-19 information serves its actual purpose
The extent to which using the COVID-19 information arouses positive feelings
(Fassnacht and Koese, 2006)
Outcome Constructs Value
Satisfaction
Quality of Life
Users’ trade-off between benefits and costs
Users’ affect with (or feelings about) prior COVID-19 health-care service use
QOL is defined as a sense of overall well-being in health
(Parasuraman et al., 2005) (Choi et al., 2007)

Sample demographic information

Variables Frequency (n = 1,008) (%)
Gender
Male
Female
587
421
58
42
Age
18–28 years
28–37 years
38–47 years
48–57 years
58 and above
516
181
144
98
69
51
18
14
10
7
Income
5,000 SR or below
5,001–10,000 SR
10,001–15,000 SR
15,001–20,000 SR
20,001 SR or more
311
178
212
155
152
31
18
21
15
15
Education
High School
College Diploma
Bachelors
Masters
Doctorate
132
157
278
290
151
13
16
27
29
15
Marital status
Single
Married
622
386
62
38

Measurement model results

Items SL SE T-Value VIF CR AVE
COVID-19 health system quality 0.94 0.37
COVID-19 system quality 0.90 0.43
System reliability 0.80 0.57
I believe during the COVID-19 health-care service system is working smoothly 0.57 0.03 18.79 2.12
I believe during the COVID-19 health-care service system performs reliably 0.56 0.03 16.75 2.54
I believe during the COVID-19 health-care service system is dependable 0.58 0.04 16.55 2.01
System efficiency 0.78 0.54
During the COVID-19 health-care service system is simple to use 0.60 0.03 18.72 2.35
During the COVID-19 health-care service system is easy to use 0.55 0.03 17.31 2.12
During the COVID-19 health-care service system is well organized 0.53 0.03 15.42 1.86
System flexibility 0.77 0.53
During the COVID-19 health-care service system can be adapted to meet a variety of health care needs 0.57 0.03 18.96 2.12
During the COVID-19 health-care service system can flexibly adjust to new health-related demands and conditions 0.56 0.03 19.34 1.99
During the COVID-19 health-care service system is versatile in addressing needs as they arise 0.53 0.03 17.50 1.86
System privacy 0.71 0.46
I believe during the COVID-19 health-care service delivery, it protects my personal information 0.47 0.03 13.80 1.74
I believe during the COVID-19 health-care service delivery, it does not share information with others 0.50 0.04 13.70 1.70
I believe during the COVID-19 health-care service delivery, it offers me a meaningful privacy guarantee 0.62 0.03 23.46 2.26
COVID-19 interaction quality 0.85 0.40
Responsiveness 0.75 0.50
During the COVID-19 health-care professionals are always willing to help me 0.61 0.03 19.44 2.15
During the COVID-19 health-care professionals show interest to solve my problems 0.74 0.03 29.37 2.29
During the COVID-19 health-care professionals provide service right at the first time 0.62 0.03 20.32 1.75
Assurance 0.74 0.48
During the COVID-19 health-care professionals’ behavior instills confidence in me 0.62 0.03 20.66 1.93
During the COVID-19 I feel safe while consulting with health-care professionals 0.60 0.03 19.59 1.96
Health-care professionals serving during the COVID-19 are competent in providing service 0.67 0.03 24.67 1.94
Empathy 0.63 0.41
During the COVID-19 whenever necessary health-care professionals give me personal attention 0.66 0.03 24.68 1.85
During the COVID-19 whenever necessary health-care professionals give me individual care 0.67 0.03 21.78 2.17
During the COVID-19 whenever necessary health-care professionals understand my specific needs 0.32 0.04 9.01 1.26
COVID-19 information quality 0.87 0.52
Utilitarian information 0.82 0.61
During the COVID-19 information from health-care service system serves its purpose very well 0.72 0.03 28.98 2.46
During the COVID-19 information from health-care service system is provided according to my needs 0.60 0.03 17.98 2.37
During the COVID-19 information from health-care service system is very useful to me 0.63 0.03 19.93 2.53
Hedonic information 0.80 0.56
During the COVID-19 I feel hopeful as a result of having information 0.74 0.03 27.93 2.14
During the COVID-19 I feel encouraged having this information 0.74 0.03 26.50 2.13
During the COVID-19 I believe my future health will improve having this information service 0.75 0.03 29.79 2.02
Service system satisfaction 0.84 0.56
I am satisfied with my use of COVID-19 health service system 0.81 0.02 38.05 1.69
I am contented with my use of COVID-19 health service system 0.73 0.02 34.64 1,88
I am pleased with my use of COVID-19 health service system 0.75 0.02 36.71 2.37
I am delighted with my use of COVID-19 health service system 0.70 0.03 28.13 2.13
Quality of Life 0.79 0.48
COVID-19 service system enabled me to improve my overall health 0.74 0.03 26.51 1.59
In most ways, my life has come closer to my ideal as I started using COVID-19 health-care service system 0.73 0.02 30.60 2.02
I have been more satisfied with my health life, thanks to COVID-19 health service system 0.68 0.03 26.93 1.76
So far, COVID-19 service has helped me to achieve the level of health I most want in life 0.62 0.03 18.50 1.32
Notes:

SL = Standard loadings; SE = Standard error; VIF = Variance inflation factor; CR = Composite reliability; AVE; Average variance extraction

Means, standard deviations, correlations, reliabilities and validity estimates

Variables M SD CA 1 2 3
1. Health system quality 4.39 1.17 0.94 0.61 0.82 0.76
2. Service system satisfaction 4.68 1.49 0.84 0.73** 0.75 0.75
3. Quality of life 4.55 1.41 0.78 0.65** 0.61** 0.69
Notes:

n = 1,008. M = Mean; SD = Standard deviation; CA = Cronbach’s α; Italicfaced diagonal elements are the square roots of the AVE statistics for discriminant validity by Fornell–Larcker criterion. Below the diagonal elements are the correlations between the constructs. Above the diagonal elements are the Heterotrait-Monotrait Ratios.

**

p < 0.01; *p <* 0.05, two-tailed

Estimates of the direct and indirect effects

Path relations β SE LLCI ULCI VAF t-value p-value R2 f2 Q2
Direct effects
HSQ  SAT 0.83 0.01 0.80 0.85 58.02. 0.00 0.69 2.19 0.36
SAT  QOL 0.37 0.08 0.22 0.53 4.68 0.00 0.63 0.12 0.28
HSQ  QOL 0.46 0.07 0.31 0.60 6.23 0.00 0.17
Indirect effects
SSO  SAT  QOL 0.31 0.07 0.06 0.19 0.40 4.64 0.00
Notes:

HSQ = Health system quality; SAT = Service system satisfaction; QOL = Quality of life; β = Standardized path coefficient; SE = Standard error; LLCI = Lower limit confidence interval; ULCL = Upper limit confidence limit; VAF = Variance accounted for. Indirect effects were tested using the bootstrapping procedure with 5,000 bootstrap samples

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Further reading

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Vargo, S.L. and Lusch, R.F. (2008), “Service-dominant logic: continuing the evolution”, Journal of the Academy of Marketing Science, Vol. 36 No. 1, pp. 1-10.

Acknowledgements

This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (G: 419-120-1439). The authors, gratefully acknowledge DSR technical and financial support.

Corresponding author

Mohammad Asif Salam can be contacted at: mbamas@yahoo.com

About the authors

Mohammad Asif Salam is based at the Department of Business Administration, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia. He (masalam1@kau.edu.sa) is currently an Associate Professor of Marketing and Supply Chain Management at the King Abdul Aziz University of Saudi Arabia. Before his current position, Dr. Salam has served several Canadian universities like Ryerson University, University of the Fraser Valley and Mount Allison University. In 2004, he earned Doctorate in Business Administration. His academic research focuses on interdisciplinary issues in marketing and supply chain management, buyer-supplier relationship, sustainability, corporate social responsibility in purchasing and supply chain, health-care logistics, lean and agile logistics and humanitarian disaster logistics. Dr Salam has published in numerous academic peer-reviewed journals include: the European Journal of Marketing, Industrial Marketing Management, Industrial Management & Data Systems, Journal of Business Ethics, Journal of Enterprise Information Management, Benchmarking: An International Journal, International Journal of Procurement Management, International Journal of Logistics and Transport, Journal of Supply Chain Management: Research and Applications, International Journal of Services and Operations Management. He has received several national and international research grants and has been active in research across a wide number of topics related to logistics and supply chain management. He also has served the United Nations Peacekeeping Mission in Mozambique.

Saleh Bajaba is based at the Department of Business Administration, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia. He (sbajaba@kau.edu.sa) is an Assistant Professor of Management and the Director of the MBA and EMBA programs at King Abdulaziz University. Saleh holds a Doctor of Business Administration in Management from Louisiana Tech University, a Master of Science in Financial Engineering from New York University and a Bachelor of Science in Business Administration from King Abdulaziz University. He is a licensed Certified Treasury Professional (CTP) from the Association of Financial Professionals in the USA. Saleh’s previous work experience includes an Instructor of Management at Louisiana Tech University, a Coordinator of the Accreditation Unit at the Faculty of Economics and Administration at King Abdulaziz University and an Assistant to the Executive Managing Director for Development at the Saudi Binaden Construction Group. His work has appeared in journals such as Personality and Individual Differences, Applied Psychology: International Review, Current Psychology and others. His work also presented at conferences such as annual meetings of the Academy of Management, Southern Management Association, Eastern Academy of Management and Midwest Academy of Management. He won several awards related to research activities. He is an Ad hoc reviewer for several international journals. He is also an active member of the Academy of Management Association, Southern Management Association, Midwest Academy of Management Association and Eastern Academy of Management Association. His primary eight research interest focuses on proactivity and adaptivity in the workplace, leadership, organizational identification and mindfulness. His dissertation is on tempered radicalism.

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