1 Introduction

The COVID-19 pandemic emerged in Wuhan, China, spread worldwide, and caused considerable health and life threats (Lv et al. 2022). COVID-19 disease has had broad effects on the global economy and regional and international trade (Gardas and Navimipour 2021; Liu et al. 2022). Coronavirus's epidemic has created unprecedented limitations in the range of popular activities (Stribling et al. 2020; Vahdat 2022). It has reduced broad economic activity, especially in essential sectors such as the service sector, including small and large businesses, restaurants, hotels, and the health sector (Soto-Acosta 2020; Vahdat et al. 2021a, b). Apart from other economic sectors, the health sector has faced a sharp increase in demand for healthcare in all parts due to its strong presence in the fight against the virus (Ahmed 2019; Ding et al. 2020). It causes the growing demand for healthcare services since the high amount of COVID-19-related death puts pressure on healthcare systems (Chen and Zhao 2020; Vahdat et al. 2021a, b).

Health services improvement is a well-established assignment worldwide (Tao and Wang 2020). Over 40 years ago, it was the primary concern of the “health for all” slogan at the World Health Organization’s (WHO) landmark basic healthcare conference in Alma-Ata (Gillespie 2019; Paina and Peters 2012). Through the community of health, there is a matter that health intervention programs are frequently solely supply-oriented and deny the social factors restricting the necessity for impressive utilization of health services (Ahmed et al. 2010; Landi et al. 2018). Information Technology (IT) profoundly impacts various jobs, improving job cost and quality (Silva et al. 2018). Healthcare institutions are no exception (Dahleez et al. 2021). Healthcare systems are the most complex endeavors (Churruca et al. 2020; Rawat et al. 2021). Electronic Health Services (EHS) are utilized by suppliers, policymakers, patients, doctors, workers, and other healthcare staff. EHS contains numerous pros, like decreasing healthcare fees and providing more abrupt and impressive processing (Brinkerhoff et al. 2019). Albeit, the utilization of the EHS enhances the concerns of privacy, security, and integrity of healthcare data (DeCicca et al. 2017). Healthcare organizations significantly influence any society (Ampaw et al. 2020). These organizations' electronic processes can greatly elevate the quantity and quality of services and the community's health status (Batterham et al. 2016). The health sector in all health systems has always faced unlimited potential demand and limited actual resources. One of the goals is proper resource allocation, equitable distribution of facilities and services, and healthcare.

Therefore, this paper evaluates the development factors of modern clinical and health services in the COVID-19 era. Novel IT improvements such as Internet of Things (IoT) (Heidari et al. 2020) and blockchain (Dehghani et al. 2020) have grown into domains that technology applications and aimed subscribers may widely distinguish. In order to surpass or even persist, most professions depend on and speed up IT investments (Jamali et al. 2020). Now, diverse IT applications formulated for supporting particularized services and tasks by single professionals have grown abruptly. Therefore, technological factors can influence modern clinical and health services development. Also, one of the important factors for the development of modern clinical and health services is management factors. A low percentage of papers have addressed the development of modern clinical and health services in terms of environmental, social, and cultural factors in a comprehensive model (Abera et al. 2014). A notable potential for e-health exists to transfer quality, cost-effective healthcare and pay for the systems of e-health by healthcare systems and governments. However, there is compression between e-health utilization in current and execution (Ross et al. 2016). Therefore, our investigation aims to examine these four factors (technological, management, social and cultural, environmental factors) as the main factors influencing the development of modern clinical and health services in the COVID-19 era.

The main question that this research intended to answer is: “Which factors are significant in developing modern clinical and health services during COVID-19?”. Moreover, this research makes the following contributions:

  • Extending our comprehension of how to define and evaluate the development of modern clinical and health services in the COVID-19 era

  • Providing a framework for assessing the impact of technological, management, social, cultural, and environmental factors on the developing modern clinical and health services

  • Contributing to a deeper knowledge of e-health system development and promotion methods

  • Examining prospective clinical and health-care concerns

The background and related work are presented in the following section. The hypotheses and conceptual model are presented in Sect. 2.3. The methodology has been explained in Sect. 3. The outcomes are presented in Sect. 4. In Sect. 5, the discussion and implications have been described. Finally, the conclusions and limitations of the research are provided in Sect. 6.

2 Background and related work

This section discusses clinical and health services, and related work.

2.1 Clinical and health services

The Coronavirus crisis quickly shifted from a medical, health, and local issue to a social, political, economic, and cultural issue, arguably humanity's most global problem (Abbasi et al. 2021; Demeco et al. 2020; Shammi et al. 2021). A topic that has sparked a great debate nowadays is the impact of COVID-19's dissemination on enhancing contemporary health and clinical services, which has generated some difficulties. Since the late 1980s, the utilization of health IT in healthcare has grown in popularity. Early health IT was primarily concerned with digitalizing conventional public health procedures. The word has gotten increasingly generic as new technologies have emerged. In the last 20 years, there has been a continuous growth in research interest in eHealth-assisted patient care (Scholz et al. 2021). The concern for the “excellent health and well-being” of the world's population is prioritized in the third goal of sustainable development. People in underdeveloped nations strive to obtain excellent health and well-being since they are regularly endangered by communicable diseases and are unable to routinely consult in a health facility due to a lack of financial means (Kwamba et al. 2021). Despite the challenges posed by the condition, healthcare institutions have been given an unanticipated chance to implement changes and urgent advances in the service delivery paradigm that would have met significant opposition prior to COVID-19. Due to the intricacy of healthcare systems, including all stakeholders is essential in meeting users' requirements and responding to the entire population (Bonciani et al. 2022). Health systems' investigation has been described broadly-fairly by promoting the potency and usefulness of the health system as a consolidated part of the total socioeconomic progress procedure (Caprara et al. 2019; Chung et al. 2016; Ramtohul 2015). Health services' investigation has been explained more carefully relevant to the connection between health service transfer and the health requirements of the crowd: for instance, as 'the detection of the healthcare necessities of communities and the investigation of the preparation, usefulness, and the utilization of health services’ (Bowling 2014; Meri et al. 2019). Also, a few pieces of research have addressed the development of modern clinical and health services in the contextual factor or terms of human characteristics (Carausu et al. 2017; Kim et al. 2016).

2.2 Related work

Using articles written in health and clinical services development, this study provides a model and framework to examine and test influential factors in the development of modern clinical and health services in the COVID-19 era. Researchers have revealed that the utilization of EHS has increased over time (Huo et al. 2018; Zou et al. 2020). The rest of this section provides a brief overview of the state-of-the-art research in the field of modern clinical and health services.

Throughout the Coronavirus in Southeastern Idaho, Schow et al. (2022) employed investigation as an intervention approach to investigate telebehavioral health services. They wanted to describe supplier perspectives, develop policy and practice suggestions, and enhance community stakeholders' understanding of telebehavioral health. They conducted and analyzed semi-structured interviews using a conceptual approach and treatment technique. The suppliers offered practical scenarios that tackled technology and training, access to care, safety, shifting provider roles, reimbursement for services, therapies that are not well matched to telehealth, and the complexities of living and working in freshly created spaces of care. Rahi et al. (2021) used three popular concepts, including the Protection Motivation Theory (PMT), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the DeLone & McLean's information success model, to analyze patients' attitude regarding the implementation of telemedicine health services. The findings revealed that these ideas described 80.4 percent of the variation in patient attitudes regarding telemedicine health care uptake. Furthermore, the findings revealed that healthcare personnel might improve patient sentiments about telemedicine health services by improving computer self-efficacy, service quality, and performance expectancy.

Gruber et al. (2021) reviewed mental health and clinical psychological science in the COVID-19 era. In this paper, COVID-19 was theorized as a compounding and multidimensional stressor that will produce a great requirement for intervention. The article is descriptive. Besides, they discussed the problems and possibilities in the domain of psychological science. Clinical psychology scientists should be willing to speak out in scientific journals and on the airways with colleagues from other scientific areas to gain a leadership position. Al-Radaideh and Alazzam (2020) discovered what variables impact users' decisions on utilizing a cloud-based health awareness system. In four locations around Jordan, 366 people were questioned using questionnaires. Data were analyzed using SPSS. It is evident from the outcomes that the effort anticipation, efficiency facilitating cases, social effect, data privacy, and information exchange impact consumer's behavior purposes. However, cloud-based health had been identified as a numerically insignificant factor. Also, Kiberu et al. (2021) developed e-health readiness assessment frameworks based on the evidence-based framework (cross-sectorial and literature expert idea). The results showed that depending upon previous utilization of a management tool (PESTEL), subscribers’ consensus on factors necessary for preparation evaluation has been equal with PESTEL’s six fields: economic, political, technological, sociocultural, legal, regulatory, and environmental.

Moreover, Razmak et al. (2018) presented patients’ behavioral desires to utilize e-health applications, connecting technological and human viewpoints by improving and examining a holistic model. The Partial Least Squares (PLS) tool measurement was utilized to evaluate the reliability of investigation structures. The twelve structures were examined one by one with quantitative data like factor analysis and multiple, single, and hierarchical-multiple regression. The outcomes showed that the sociological, technological, organizational, and psychological factors positively and notably influence patients’ e-health application adoption. Also, Hoque et al. (2017) investigated the factors that affect the acceptance and utilization of e-health applications from patients’ viewpoints. They developed the Technology Acceptance Model (TAM), containing trust and privacy factors. A constructed questionnaire was utilized to gather 350 subscribers or more data in Dhaka's diverse public and private hospitals. The data were examined employing the PLS and SEM. Their investigation specified that perceived ease of use, usefulness, and trust were critical elements affecting the desire to adopt e-health. Privacy was detected as a lower-important factor in the e-health context in Bangladesh. It could also be deducted from the outcomes that gender was relevant to the assumption and utilization of EHR. Finally, Sulaiman and Magaireah (2014) explored the factors that significantly influence the assumption of cloud-based consolidated e-health record EHR systems in healthcare institutions in Jordan, depending on the Technology, Organization, and Environment (TOE) scenario. The results showed that three TOE contexts that impact cloud-dependent EHR adoption are environmental context (legal environment, competition, and government policy), technological context (security, reliability, and privacy), and organizational context (technology readiness and top management support).

The next section refers to the conceptual model and research hypothesis according to the literature reviewed in this section. We intend to provide a comprehensive model by identifying the variables affecting modern health and clinical services and examining and analyzing it in the COVID-19 era.

2.3 Research hypothesis and conceptual model

These e-health improvements emphasize the significance of evaluating the width to which the digital technology utilization for health strength elevates health communication, access to health services, and access to information for customary vulnerable nations (Mesch 2016; Osman and Bennett 2018). EHS has been widely utilized by patients, policymakers, workers, suppliers, doctors, and other healthcare staff (Johnson et al. 2020). EHS contains several pros, like lowering healthcare fees and supplying abrupt and useful processing. Albeit, the utilization of EHS enhances the concerns of privacy, security, and the combination of healthcare data (Minton and Cornwell 2016). They influence patients’ desire to reveal their data for healthcare; it may also result in life-menacing yields (Yüksel et al. 2017). According to the literature, four factors were identified as influencing variables in the development of modern clinical and health services: technological factor, management factor, social and cultural factor, and environmental factor. As illustrated in Fig. 1, the technological factor includes information systems, IoT, and technical infrastructure indicators. The management factor also comprises strategy, knowledge and expertise, commitment, and support indicators. Moreover, the social and cultural factors include indicators such as attitude, skill, and education. Furthermore, the environmental factor comprises government policy, credibility, and compatibility indicators. Finally, perceived usefulness and ease of use are the two mediating variables considered in this study that influence modern clinical and health services during COVID-19. Therefore, the hypotheses are:

Fig. 1
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A framework for the development of modern clinical and health services in the COVID-19 era

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H1-a

Social and cultural factors positively affect the relative perceived usefulness.

H1-b

Social and cultural factors positively affect the relative perceived ease of use.

H2-a

Management factors positively affect the relative perceived usefulness.

H2-b

Management factors positively affect the relative perceived ease of use.

H3-a

Technological factors positively affect the relative perceived usefulness.

H3-b

Technological factors positively affect the relative perceived ease of use.

H4-a

Environmental factors positively affect the relative perceived usefulness.

H4-b

Environmental factors positively affect the relative perceived ease of use.

H5-a

Perceived usefulness positively affects the relative development of modern clinical and health services.

H6-b

Perceived ease of use positively affects the relative development of modern clinical and health services.

3 Methodology

The SEM is a prevalent statistical technique considered the “most fully developed and general system”. The assessment and architectural models are the two major models in this system. The “outer model,” which assesses the links across the latent concept and its pertinent indicators, is the measuring model. The latter pertains to the “inner model,” which assesses the links among the components (Al-Emran et al. 2018). A common guideline for doing research methods is shown in Fig. 2. The present section describes the instrument, data gathering mechanisms, and analysis.

Fig. 2
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The flowchart of the research methodology

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3.1 Instrument

The structured questionnaire method is utilized to gather pertinent data. In the items, the 5-point Likert Scale is used. 1 indicates significant disagreement, 3 indicates the scale's middle, and 5 indicates great agreement. When the scale points are fewer than five or more than seven, data from Likert items becomes much less reliable. Also, it is possible to compare reliability coefficients with other research using five-point Likert scales. In addition, with a 5—point scale, it is pretty simple for the responder to read out the complete list of scale descriptors. The questionnaire was divided into two sections. The first contained basic demographic data like gender, age, and educational credentials. The model's entire components were measured using questionnaires in the second section. Every measure in the recommended method's latent construct was obtained from prior research. Before finishing the questionnaires, a pilot study was undertaken to obtain meaningful input on their usefulness, and appropriate changes were made. The PLS approach was used in conjunction with the SEM methodology in this study. Path modeling with the SEM-PLS is widely employed to test various suggested models and assumptions.

As suggested by (Lohmöller 1989), the basic PLS process includes two phases. The iterative estimate of latent variable scores is the initial phase, which consists of a four-step technique that is performed until convergence or the greatest possible number of iterations is achieved. The path coefficients are evaluated in the second phase (Fig. 3) (Abusafiya and Suliman 2017; Sun et al. 2021).

Fig. 3
figure 3

The flowchart for the PLS

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3.2 Data collection

The 35 respondents completed the pretest survey before the ultimate assessment to give us with their initial understanding of the material. Pretesting is a means of ensuring that questions function as intended and that persons who are likely to reply to them understand them (Hilton 2017). Online questionnaires (based on the Telegram APP) were published for a month among decision-makers, managers and employees of the healthcare sectors in Tianjin, China. Respondents were chosen using a basic random procedure to ensure that everyone had an equal chance of getting chosen. We described our investigation's target and requested their involvement to elevate precision and answer rate via the procedure. Ultimately, 300 questionnaires were dispatched in a chance confrontation. 225 questionnaires were attained from respondents, and 23 incomplete questionnaires were not contained in the analysis. It is almost impossible to evade bias because of non-response, but there are ways to reduce the likelihood of its occurrence. The following strategies have been considered to avoid bias.

  • The online questionnaire used in this study was compatible with different devices.

  • A reasonable period was set for data collection.

  • Graphic design was considered in the questionnaire

  • Respondents have been assured of confidentiality.

  • The statistical sample size is considered more prominent.

3.3 Data analysis

SmartPLS 3.0 was utilized for data analysis after supplementary the ultimate data to respond to the study's question and examine the hypothesis. Here, we have utilized a two-level method to analyze the data (Wahyuni 2017). The measurement model is evaluated in the first step, and the hypothesis is tested in the subsequent. The outer model was used to investigate the data validity and reliability. Face validity, concept validity, and content validity are employed to investigate instrument validation in this work. Face validity is a subjective assessment of a construct's operationalization. It means that face validity points to investigators' subjective judgments of the measurement tool's organization and relevance, such as whether the items in the instrument seem to be relevant, rational, unambiguous, and clear. Content validity is the process of evaluating a new survey instrument to verify that it has all of the necessary questions while excluding those that are irrelevant to a certain construct area. The judgemental technique for determining content validity is conducting literature studies and then having professional judges or panels evaluate the results. The operationalization of a notion, concept, or action that is a construct relates to how successfully you converted or changed it into a working and operating reality. Convergent and discriminant validity are two aspects of construct validity (Taherdoost 2016). By studying the internal consistency reliability and severe loadings number and assessing the CR and Cronbach's Alpha, the reliability indicator was used to test the reliability. Convergent validity was explored using the Average Variance Extracted (AVE) method and discriminant validity. In SmartPLS, the hypothesis was tested using Bootstrap.

4 Results

The results of PLS's measurement and structural model analysis are described in this section. The factors should be reviewed before the scales are analyzed to determine their suitability for factor analysis. There are three approaches for determining if data sets are suitable for factor analysis. A correlation matrix, Kaiser–Meyer–Olkin (KMO) test, and Barlett test are three of these approaches. The Barlett test examines the likelihood of high-rated associations in at least some correlation matrix variables. The null hypothesis “correlation matrix is the unit matrix” should be disproved to proceed with the study. The null hypothesis is rejected because there are substantial correlations between variables; it means that the data set is suitable for component analysis. The KMO sample adequacy criteria is a measure that compares the magnitude of the reported correlation with the partial correlation coefficient. The richer the data set for factor analysis, the greater the ratio (Tastan and Yilmaz 2008). The KMO value can range from 0 to 1. KMO values from 0.8 to 1.0 suggest acceptable sampling. KMO levels of 0.7 to 0.79 are average, whereas values of 0.6 to 0.69 are poor. KMO values below 0.6 suggest insufficient sampling and the need for corrective action. If the number is under 0.5, the factor analysis findings will almost certainly not be appropriate for data analysis. The mean commonality of the preserved items must be checked if the sample number is less than 300. For samples under 100, an average value > 0.6 is appropriate; for sample sizes from 100 to 200, an average value from 0.5 to 0.6 is appropriate (Shrestha 2021). Table 1 reveals that the KMO statistics value is 0.840 > 0.6, indicating that enough sampling and factor analysis are appropriate for the data. The appropriateness of the correlation matrix is tested using Bartlett's test of sphericity. According to Bartlett's test of sphericity, the correlation matrix contains large correlations across at least some variables, which is highly significant at p 0.001.

Table 1 Barlett test and Kaiser–Meyer–Olkin (KMO) test
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According to these results, it could be said that available data sets were suitable for factor analysis.

4.1 Measurement model

The measurement model was estimated by testing the internal reliability, convergent validity, and discriminant validity. CA and CR were utilized to assess the internal reliability, and a level of 0.70 was evaluated as an acceptable internal consistency indicator (Hoque et al. 2017). The validity was evaluated with the consideration of discriminant and convergent validity, where the value of item loadings and AVE had to be > 0.5 to achieve the convergent validity (AlBar and Hoque 2019; Wu et al. 2022). Before forming any assumption, the structural model needs to be thoroughly validated in addition to testing the calculation model. Collinearity is a possible concern in the structural model, and the Variance Inflation Factor (VIF) values of 5 or more usually suggests such an issue (Wong 2016).

CR, Internal consistency reliability, and CA all have levels over 0.7 in Table 2, indicating good internal consistency for each dimension's measure items. The factor loadings and AVE values are both over the specified thresholds of 0.70 and 0.50, indicating that convergent validity standards were fulfilled (Adu et al. 2019). Also, the outcomes of the outer loadings and VIF are shown in Table 3.

Table 2 Reliability and average extracted variance
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Table 3 Statistics of confirmatory factor analysis (outer loading and VIF)
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Discriminant validity examines if measurements are relevant or not. The square root of the AVE amount for latent variables should be greater than the correlation value with other latent variables to pass the discriminant validity test (Damayanti et al. 2018; Hair et al. 2012). We discovered that the indicator values have the maximum quality of the other construct indicators based on cross-loading and AVE square root. It demonstrates that the discriminant validity test is passed by this model (See Table 4).

Table 4 Discriminant Validity
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The HeteroTrait-MonoTrait (HTMT) ratio has been proven reliable in parameter estimation. Furthermore, the HTMT is better and more impartial than the conventional and widely used Fornell-Larcker criteria (Johan et al. 2020). To prove discriminant validity, the proposed criterion is less than 1 or less than 0.85 (Henseler 2017). As indicated in Table 5, the study assesses discriminant validity using the conservative HTMT 0.85 ratio.

Table 5 Discriminant validity (HTMT0.85 criterion)
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4.2 Structural model

The structural model is illustrated in Fig. 4 and Table 6. The path significance stages were estimated with the utilization of Bootstrap with 500 repetitions of resampling. Figure 4 illustrates the path coefficients, significance levels, and R2. The R2 was utilized to assess the model. Totally, the model describes 82.4%, 73.6%, and 80.0% of the variance in perceived usefulness, perceived ease of use, and development of modern clinical and health services, respectively.

Fig. 4
figure 4

The structural model for the development of modern clinical and health services in the COVID-19 era

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Table 6 The results of the smart PLS
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To build the link between the model's components, path coefficients and t-values for structural paths were generated. All the hypotheses were examined. The t-values presented in Table 6 with the PLS analysis results were computed using a bootstrapping resampling approach.

The drawback of PLS route modeling is the lack of a universal scalar function and global Goodness-of-Fit (GoF) measurements. To address this, Tenenhaus et al. (2005) developed the GoF, which considers the performance of both structural and measurement models simultaneously. The GoF may be calculated using the AVE and R square of the structural design. Setting the number to 0.36 is the best option (Wetzels et al. 2009; Yang et al. 2017). Our research model has a GoF of 0.68, as determined by Henseler and Sarstedt (2013). Thus, it is valid.

The endogenous construct is predictive if the Q2 score is positive. It's customary to test if the model is able to predict the reflected indications. The Q2 test by Stone-Geisser evaluates the models' out-of-sample prediction ability. The predictive relevance Q2 for the intention in the path model is 0.436, indicating that the model has a significant predictive significance (Johan et al. 2020).

4.3 Importance-performance map analysis

According to (Ringle and Sarstedt 2016), importance-performance map analysis aims to figure out which elements have poor efficiency but are extremely important to the constructs. It is resilient and feasible in PLS-SEM, allowing for a more functional extension of traditional path coefficient estimations (Thaker et al. 2021). Five precursors anticipate the emergence of current clinical and health services in COVID-19 in this study (i.e., management, technology, environmental, social and cultural, perceived utility, and perceived ease of use). The analysis was completed, and the results are shown in Fig. 5. According to Fig. 5, none of the variables are low priority or probable overkill. Table 7 contains a comprehensive list of the values.

Fig. 5
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Importance performance map analysis

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Table 7 The results of the IPMA statistics
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The produced values of the map analysis statistics utilizing SmartPLS 3 software are shown in Table 7. There are two aspects to it: significance and performance. Based on Table 7, results indicated that the top two variables with high performance in the development of modern clinical and health services were management factor and perceived usefulness, with index values of 74.677 and 73.225, respectively. Next, social and cultural factor and technological factor were in the third and fourth places with index values of 72.365 and 71.866. It was followed by perceived ease of use and environmental factor with index values of 71.670 and 70.105.

5 Discussion and implications

With the emergence of the coronavirus, millions of people's lives and health have been threatened. This epidemic causes high mortality due to viral infections and causes psychological catastrophe in all parts of the world. The unpredictability and uncertainty of the spread of an infectious pandemic has a high potential for psychological fear of disease transmission and often leads to many psychological problems. E-health services and systems may transfer notable developments to elevate the health sector's efficiency. They suggest a method with efficiency attainments to taxpayers and governments to handle enhancing requests in healthcare services. They also assist in reforming the delivery of healthcare, turning it into a more citizen-concentrated form. Some bright e-health targets are evidence-depended medicine (via access to web-available case repositories), dealing with privacy and security contexts, quality of care and greater efficiency, widening the scope of healthcare, education of physicians through online sources, empowering consumers and patients, and having equal access to healthcare (Goulão 2014). E-health services and systems may transfer notable developments to elevate the health sector's efficiency. They suggest a metho, with efficiency attainments to taxpayers and governments to handle enhancing requests in healthcare services. They also assist in reforming the delivery of healthcare, turning it into a more citizen-concentrated form. Our investigation checked out the factors affecting the development of modern clinical and health services in the COVID-19 era. With the utilization of a questionnaire, the data was gathered. Also, factor analysis was performed employing smart PLS 3.0 and IBM SPSS statistics. The outcomes showed significant relationships between social and cultural factors and perceived usefulness (β = 0.149, t = 2.352) and between social and cultural factors and perceived ease of use (β = 0.173, t = 2.157). Thus, H1a and H1b are supported. The social and cultural factors include indicators such as attitude, skill, and education. Also, the management factor and perceived usefulness (β = 0.334, t = 5.078), management factor, and perceived ease of use (β = 0.175, t = 2.697) are significant. Thus, H2a and H2b are supported. The management factor includes strategy, knowledge and expertise, commitment, and support indicators. Technological factors are affirmatively relevant to perceived usefulness (β = 0.410, t = 5.877), technological factor, and perceived ease of use (β = 0.281, t = 3.461), in support of H3a and H3b. The technological factor includes information systems, IoT, and technical infrastructure indicators. The results show that the relationships between an environmental factor and perceived usefulness (β = 0.104, t = 2.136), environmental factor, and perceived ease of use (β = 0.341, t = 4.901) are significant. Thus, H4a and H4b are supported. Furthermore, the environmental factor includes government policy, credibility, and compatibility indicators. Therefore, outcome quality is positively relevant to the perceived usefulness and development of modern clinical and health services (β = 0.554, t = 8.023), supporting H5. Finally, result quality is positively relevant to the perceived ease of use and development of modern clinical and health services (β = 0.395, t = 5.619), supporting H6.

5.1 Theoretical contribution

This research represents a worthwhile direction by examining the development of modern clinical and health services during COVID-19. It implicitly contributes to the knowledge and literature by highlighting modern clinical and health services performance. The paper has also analyzed the model in a novel setting (China) and a new era (COVID 19). It has also examined the role of technological factors, management factors, social and cultural factors, environmental factors, perceived usefulness, and ease of use in the development of modern clinical and health services during COVID-19. This research extends the theoretical prospect and the present understanding of the key elements of the development of modern clinical and health services. It explains how these elements make the development of modern clinical and health services.

5.2 Practical implications

In real-world scenarios, the outcomes back the key role of the subsequent issues: technological factors, management factors, social and cultural factors, environmental factors, perceived usefulness, and ease of use. This research provides better support for policymakers and stakeholders, which may help design and implement appropriate interventions in various cases to improve overall health. The results showed that the main obstacles to the development of modern health and clinical systems and services include lack of a definite strategy, ambiguity and complexity of IT infrastructure, rapid changes in managers, lack of clear mechanisms for financing the electronic health system, lack of technical standards, weaknesses in health implementation method, inability to attract IT experts in the e-health domain, and the problem of culture and training related to training and practice for Information Communication Technology (ICT) skill. Therefore, managers are recommended to pay special attention to improving these cases and removing these obstacles. Also, according to the results, one of the most important measures to deal with the scarcity of resources during the epidemic is to adopt on-time and appropriate policies, which is a key strategy to overcome the crisis. International experience shows that COVID-19 health and economic shocks can be reduced through timely policy measures focusing on identifying and supporting those in need and thus allocating resources properly. The WHO recommends the government remove financial barriers and cut costs for COVID-19 patients to pay for treatment in the early stages of policy-making to address the coronavirus. In fact, public mobilization for health should be done with government repayments in this area, and government financial support in the field of health should continue.

6 Conclusion, future work and limitations

After the COVID-19 spread, the world faced many challenges, including the secretary-general of the united nations, which was considered larger and more influential than World War II. The disease has greatly affected today's political, economic, and sociocultural spheres. It is expected to create a fifth wave in the world called the post-COVID-19 era, which will change the trend and international, national, and local relations. The development of healthcare services in developing and developed nations via IT and other factors has received particular attention from medical researchers, practitioners, and governments. Several governments have been suggested to execute healthcare systems centered on technology, while practitioners and investigators have discussed policies that enhance technology utilization in the healthcare sector. Health-related IT applications, handing over a range of connectivity, clinical care, and service, have been considered e-health. E-health has been developed as a novel technology to attain cost savings, procedure progress, and development in healthcare. This work's primary contribution provides a model and framework to investigate the factors influencing the development of modern clinical and health services in the COVID-19 era. The measurement of these variables' impact on the development of modern clinical and health services was provided using six research hypotheses. This paper investigated the factors related to modern clinical and health service development in technological factors, management factors, social and cultural factors, environmental factors, perceived usefulness, and perceived ease of use. The results obtained from the analysis (T-Value and IPMA) show that all hypotheses are supported and confirmed. Our investigation may be an effective guideline to elevate healthcare and governance quality in developing services in the COVID-19 era. Some orientations and prognostications can be detected, although the mentioned progress's concentration is upon today's real requirements and necessities. Further research can help deeply understand the relationship between demand/supply-side factors and adequate coverage. In future work, the following problems can be considered:

  • Examining the experiences of successful countries in the field of health;

  • Studying and introducing up-to-date patterns of hospital management in leading countries using all audio-visual tools;

  • Reviewing and modeling successful structures of the health sector in leading countries with regard to the conditions and requirements of the country;

  • Examining the challenges of implementing IoT technology in the field of health;

This analysis has its limitations as any other review. As stated above, the attained outcomes depended on the population's data. So, the fundamental economic and cultural situations may detect a restriction of the current investigation to the general nature of the outcomes. The suggested model was strictly improved depending on the available literature. However, the main belonging restriction is that it has not been experimentally tested compared to an improving world notion. The current investigation will be a basis for future adoption-based studies in developing countries.