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Abstract

The study object is the doctor-patient online community, an online health service model with the greatest growth potential during the global COVID-19 pandemic. In the web-based medical market, patients are in a weak position, relying on web-based medical platform operators to ensure medical service quality. Following social exchange theory, this empirical study divides governance mechanisms into three-supervision, reputation, and communication-to determine their influence on online patients willingness to use web-based medical platforms. It also explores the moderating effect of perceived uncertainty on the influence of social exchange and transaction cost theory. A questionnaire survey was conducted to determine online patients’ attitudes toward medical service providers including physicians and medical websites. Hierarchical linear regression analysis showed the significant positive effect of the supervision, reputation, and communication mechanisms on online patients’ usage willingness. Perceived uncertainty of doctors’ behavior has a significant moderating effect on the relationship between the three mechanisms and online patients’ usage willingness. Website-perceived uncertainty moderates the relationship between the supervision and reputation mechanisms and online patients’ usage willingness. This study provides a reference for improving online patients’ usage willingness through the platform governance of Internet medical treatment.

Keywords

medical services web-based consultation governance mechanism online patient’ usage intention perceived uncertainty

Introduction

COVID-19, an infectious disease caused by SARS-COV-2 with human pandemic potential, has caused a worldwide health crisis.¹ To minimize the risk of human-to-human transmission of COVID-19, governments adopted wide-ranging “precautions” including social distancing and quarantining those at risk.² The rapid development of the Internet has significantly changed the global information landscape.³ During public health emergencies, people focus more on health. Awareness of the online health consulting community is increasing, and web-based consultation platforms are receiving more attention from medical institutions, becoming platforms addressing common or chronic diseases and providing follow-ups to reduce the offline gathering of patients. These are also helping medical institutions improve service levels, which is the most important way to increase service experience. Online healthcare communities (OHCs) support places where physicians provide primary care to patients,⁴ relieving the pressure of offline medical treatment. However, online health services have a relatively low visit-use rate;⁵ thus, improving patients’ use and purchase rates has become the focus of academic attention.

The object of this study is the doctor-patient online community, an online health service model with the greatest growth potential during the global COVID-19 pandemic. While web-based medical services provide convenience and efficiency, their inherent risk uncertainty restricts their development. Medical services are a special commodity and high-trust service, and patients may face difficulties judging the quality of services and deciding among different treatment options.⁶ In the web-based medical market, individual patients are in a relatively weak position; therefore, they rely on web-based medical platform operators to ensure the quality of medical services.

In web-based healthcare markets, medical platform operators adopt three governance mechanisms to reduce disease service uncertainty: platform supervision, reputation evaluation, and online communication. This study aims to examine the influence of these three governance mechanisms on patients’ usage willingness. This study can help platform operators understand the needs of patients, especially how to eliminate their concerns, build trust, and attract more patients to buy medical services on the website amid the perceived uncertainty of doctors’ behavior and websites.

Patients’ usage willingness

The definition of willingness as a subjective probability is that an individual exhibits specific behaviors.⁷ Based on this, consumers’ purchase intention refers to the probability of consumers making a purchase decision. Patients are the consumers of OHCs. The product of an OHC is online health advice from doctors to patients. In the process of disease diagnosis and treatment, online and offline medical consultation services provide disease-related diagnosis and treatment advice. However, patients must first choose between online and offline service channels before seeking medical treatment.⁸ Consumers’ purchase intention comprises three factors: a particular brand, the attitude of the commodity, and consumers’ external environment. Purchase intention influences consumers’ behavioral decision-making.⁷ Purchase intention refers to patients’ cognition of commodities and services, and the possibility they will purchase these in the future. As such, purchase behavior and intention are significantly correlated.

Governance mechanism

Healthcare information technology and related systems such as those providing OHCs, which support new platforms for information exchange and online communication, can transform the traditional physician-patient relationship model.⁹ Online medical consultation services refer to activities in which patients or other patient-related personnel communicate with doctors on disease-related issues and obtain their professional advice or solutions through online platforms.¹⁰ The governance mechanism, a tool for building and forming trade relationships, involves managers’ measures to manage trading activities.⁷ This study focuses on whether the governance mechanism weakens the information asymmetry between the provider and buyer of online medical consultation services to improve patients’ purchasing intention. Based on previous research¹¹ on web-based healthcare consultation markets in China, this study considers the following three governance mechanisms. First, web-based medical platform operators can supervise doctors’ online behavior. Second, medical platform operators can build reputation mechanisms such as a credit evaluation system to transmit information about physicians’ past medical services to patients. Third, platform operators can build a mechanism for communication, such as designing online communication software to improve doctors and patients’ ability to exchange information.

Influence of the supervision mechanism on patients’ usage willingness

Transaction cost theory focuses on the choice of formal governance mechanisms such as supervision and incentives to minimize opportunistic behaviors, rendering the supervision mechanism an important part of the formal governance mechanism.¹² Here, the supervision mechanism refers to the web-based medical platform to supervise the medical market, maintain order and norms, and ensure that transaction participants comply with its prescribed code of conduct.⁷ Supervision creates social pressure on one side of the transaction, forcing participants to obey the transaction rules. Supervision comprises behavior and output supervision. Note that output supervision reduces opportunistic behavior, while behavior supervision could increase it.¹³ Output supervision refers to the measurement of visible results such as delivery time, logistics efficiency, and product quality to evaluate the product production process. For example, the purchaser supervises the supplier’s manufacturing process and quality control procedures on site.¹⁴ Normally, web-based medical platforms begin checking doctors’ service, opinions, and suggestions after receiving a patient’s complaints; thus, the supervision in this study refers to output supervision. The perceived effect of platform supervision can also be used to measure the supervision mechanism.

Influence of the reputation mechanism on patients’ usage willingness

Social exchange theory suggests using informal mechanisms such as unity, trust, communication, and reputation to restrain opportunistic behavior.¹⁵ Reputation is a type of intangible property businesses obtain through honest trading. Once damaged, reputation is difficult to recover.¹³ The reputation mechanism refers to patients’ reviews of physicians’ medical consultations after each interaction.¹⁶ When the community scale is sufficiently large, reputation plays a role only when information transmission is smooth and transmission cost is low. Therefore, most web-based retail platforms establish credit evaluation systems to reduce information transmission costs.⁷ In web-based markets, the seller’s introduction and description, which are the “eyes” consumers use to perceive goods, informs and describes users’ overall feeling of these goods or services to promote consumers’ purchase or usage intentions.¹⁷ Online review, a way of network word-of-mouth communication, has the advantages of long text retention time, wide-ranging influence, and a fast propagation speed. Moreover, the quality, potency, quantity of online reviews and credibility of reviewers impact consumers’ purchasing behavior. As a form of word-of-mouth, online reviews have become an important source of information for enterprises and consumers.¹⁸

Time and space barriers mean doctors and patients cannot directly contact OHCs. Before receiving doctors’ services, patients cannot accurately know their medical level or judge whether they can formulate an appropriate medical plan according to their condition. Here, doctors may provide an incorrect diagnosis for personal gain or provide untrue and inaccurate personal information to mislead patients or shirk responsibility. Thus, patients tend to judge the quality of their doctors based on their reputation in OHCs. Based on the particularity of the medical industry, reputation mechanisms have a crucial role in patients’ decision-making. As the quality of medical services doctors provide directly determines patients’ health and life, they tend to seek medical advice from reputable doctors. Thus, an authoritative reputation mechanism promotes effective market development. Online healthcare platforms can provide quality signals for consulting services for patients’ reference by constructing a reputation mechanism for doctors, which affects patients’ purchasing decision-making process.¹⁹ Online reputation in the form of ratings and other people’s experiences can reduce information asymmetry, further influencing the patient’s decision-making process.²⁰ In this study, we use the reliability of a credit evaluation system to measure the reputation mechanism.

Influence of the communication mechanism on patients’ usage willingness

According to relational and social exchange theory, communication is an important dimension of relational norms.⁷ Communication refers to behavior wherein both parties exchange and share important and appropriate information through formal or informal channels.²¹ The communication mechanism of the online retail market relies on online communication tools provided by platforms such as Amazon’s buyer and seller information system. Full and effective communication can help both parties eliminate differences and misunderstandings and reduce information asymmetry,⁷ allowing buyers to get more targeted information directly from sellers, which eliminates the perceived uncertainty of sellers’ behavior.²² Compared with traditional markets, information asymmetry in the online retail market is more serious, and the relationship between the two parties is interdependent. If the communication mechanism of the online retail platform is not perfect, the buyer as an information weak party may be forced to give up the profit to the seller without proper persuasion, which will produce dissatisfaction.⁷ Patients are always at a disadvantage in terms of medical services with doctors because they lack an understanding of medical knowledge and drug information. Perceived interactivity is a key feature of online healthcare communities.²³ Online messages, feedback, discussions, and evaluations are the main communication channels between patients and medical staff.

The communication mechanism is an important metric for evaluating online healthcare communities. Users of the online healthcare community benefit from interactive co-produced outcomes or trusted judgments, which increase patients’ willingness to purchase online medical services.^23,24 This study measures the communication mechanism based on the perceived effect of online patients by using the online communication tools of a web-based medical platform.

Moderating effect of perceived uncertainty

Limited rationality, opportunism, and information asymmetry affect traders’ inability to accurately predict future situations, leading to uncertainty in a transaction.⁷ Transaction cost theory contends that uncertainty is a transaction attribute that affects the choice of governance mechanism. Previous research examined the moderating effects of commodity uncertainty, website operator behavior uncertainty, and demand uncertainty.²⁵ Perceived risk directly decreases consumers’ purchase intention.²⁶ Furthermore, consumer-perceived value is influenced by consumer-perceived uncertainty. The possibility and severity of adverse outcomes consumers consider in decision-making are negatively correlated with their usage willingness.²⁷ In online shopping, perceived risk directly affects consumers’ purchase intention and trust. Because consumers have a natural risk aversion psychology, they will stop buying when they perceive a higher risk. Essentially, the higher the perceived risk, the lower is consumers’ purchase intention. This study focuses on online patient perception on web-based medical platforms. However, however, because it is difficult for patients to perceive the uncertainty of the whole market, only the moderating effect of website perception uncertainty and doctor behavior perception uncertainty on the relationship between governance mechanisms and online patient use intention is examined.

Moderating effect on perceived uncertainty of physicians‘ behavior

When the perceived uncertainty of doctors’ behavior is high, the information asymmetry between the two sides of the transaction intensifies, rendering it more difficult for patients to determine whether doctors’ professional skills and medical ethics really meet their expectations before receiving medical services. Furthermore, it is more likely to produce dissatisfaction and disputes with physicians after the provision of medical services. When buyers perceive that the uncertainty of the goods is high, they spend more time and energy understanding the relevant commodity information.²⁸ The online communication function and credit evaluation system provided by web-based medical platforms are important ways for patients to understand medical services. Physicians’ behavioral perceived uncertainty stems from their professional abilities. In this study, doctors’ professional ability is used to measure their perceived uncertainty.

Moderating effect on perceived uncertainty of website management system

Perceived uncertainty regarding a website refers to buyers’ perceived uncertainty of platform operator behavior.⁷ There are many medical service providers on web-based medical platforms, and platform operators’ behavior is not always transparent; thus, patients easily perceive uncertainty regarding their behavior. This uncertainty depends on the website’s reputation. The weaker the reputation of the website, the higher the perceived uncertainty of its behavior.²⁹ Reputation is a symbol of how respected and trustworthy enterprises are in industry.²⁹ This study uses the reputation of a web-based medical platform to measure perceived website uncertainty.

Previous studies on the OHC market have not considered governance mechanisms, despite the strong link between governance mechanisms and patients’ usage willingness. Furthermore, the effectiveness of online governance mechanisms in Internet-based healthcare remains unclear. Thus, we aim to (1) examine the influence of three governance mechanisms-supervision, reputation, and communication-on patients’ usage willingness; and (2) explore the moderating effect of perceived uncertainty of physicians’ behavior and of website management systems on governance mechanisms and patients’ usage willingness.

Based on the above analysis, several hypotheses were proposed in this study (Table 1).

Table 1.

Summary of research hypotheses.

Number	Hypotheses
H1	Platform supervision positively affects the willingness of online patients to use web-based medical platforms
H2	Reputation evaluation affects the willingness of online patients to use web-based medical platforms
H3	Online communication affects the willingness of online patients to use web-based medical platforms
H4	Physician behavioral perceived uncertainty positively moderates the relationship between platform supervision and online patients’ usage willingness. When doctors’ perceived uncertainty is high, platform supervision has a greater impact on online patients’ usage willingness
H5	Physician behavioral perceived uncertainty positively moderates the relationship between reputation evaluation and online patients’ usage willingness. When doctors’ perceived uncertainty is high, reputation evaluation has a greater impact on online patients’ usage willingness
H6	Physician behavioral perceived uncertainty positively moderates the relationship between online communication and online patients’ usage willingness. When doctors’ perceived uncertainty is high, online communication has a greater impact on online patients’ usage willingness
H7	Website uncertainty negatively moderates the relationship between platform supervision and online patients’ usage willingness. When website uncertainty is high, platform supervision has a smaller impact on online patients’ usage willingness
H8	Website uncertainty negatively moderates the relationship between reputation evaluation and online patients’ usage willingness. When website uncertainty is high, reputation evaluation has a smaller impact on online patients’ usage willingness
H9	Website uncertainty negatively moderates the relationship between online communication and online patients’ willingness to use. When website uncertainty is high, online communication has a smaller impact on online patients’ usage willingness

Based on transaction cost and social exchange theory, this study quantitatively analyzes the impact of three governance mechanisms on online patients’ willingness to use web-based medical platforms, and measures whether the perceived uncertainty of websites and of physicians’ behavior influence the effectiveness of medical service governance. Figure 1 shows the research model constructed for the purposes of this study.

Figure 1.

Governance mechanisms model for web-based medical platforms.

Methodology

Instrument development

For our research, we designed a questionnaire to assess the factors influencing patients’ purchase intention regarding OHCs. To measure each structure, questions from validated tools used in previous research were compiled and adapted, and the wording modified to fit the Chinese OHC context. This scale adopts and refers to the more mature scales in China and other countries. All items are scored on a five-point Likert scale (where 1 = completely disagree, 5 = completely agree). Table 2 provides information on the scale.

Table 2.

Measurement of variables and sources of items.

Research variables	Summary of measurement content of variables	Sources of reference and modification
Platform supervision	The website will handle complaints from patients to doctors seriously; the website maintains trading order well; one of the main roles of the website is supervision	Grewal and Saini²⁹
Reputation evaluation	Physician reputation and patient evaluation are authentic; the website credit evaluation system is reliable; patients’ comments on the website are useful to the doctor	Pavlou and Gefen³⁸
Online communication	Physicians can answer my questions in time and in detail; physicians can answer questions in time and in detail via the website’s online communication function; the online communication function of the website enhances relationships between patients and physicians	Geng Zhang and Zhenyu Liu²²
Physicians’ behavior perceived uncertainty	Physicians have a very high status; physicians’ experiences are rich; no matter what kind of health problem, the doctor can always point out the problem clearly	Rubin Lai³⁹
Website uncertainty	Website has a good reputation in industry; the website is honest; the website has a high reputation	Grewal and Saini²⁹
Patients’ usage willingness	Users will visit the website in the future; users will recommend the site to relatives and friends; users will positively evaluate medical websites	Jingyi Liu⁴⁰

To evaluate the construct validity of various projects, we attempted to identify any possible ambiguities through two methods: an expert review and pilot studies. During the expert review phase, two groups of experts proficient in both languages were invited to discuss their understanding of the questionnaire items and evaluate whether the items accurately reflected the theoretical constructs. After refining the initial investigation, we conducted a pilot test with 56 responses to ensure the instrument had acceptable reliability and validity.

Furthermore, an exploratory factor analysis (EFA) was performed to measure the convergent and discriminant validity of the items. The KMO values of the independent, dependent, and moderating variables were all greater than 0.7, indicating that the items of each variable were suitable for factor analysis. Table 3 presents the results.

Table 3.

KMO and bartlett’s test.

	Variables	KMO	Bartlett
	Variables	KMO	Approx. Chi-square	df	Sig
Independent variables		0.918	2314.038	136	0
Dependent variables		0.781	468.312	10	0
Moderating variables	Physicians’ behavior perceived uncertainty	0.732	877.758	45	0
Moderating variables	Website uncertainty	0.891	1181.644	36	0

Most scales in this study were translated from English and revised for the research purposes. Therefore, before testing the hypotheses, it was necessary to conduct an EFA of all items. In this study, a principal component analysis and the variance maximum rotation method were used to perform the EFA for the independent, dependent, and moderating variables separately.Table 4

Table 4.

Exploratory factor analysis.

	Variables	Factor loading	Reliability (α)	Cumulative explanatory %
Independent variables	Supervision (S)	S1:0.652; S20.686; S4:0.508; S5:0.584; S6:0.727; S7:0.525; S8:0.636	0.729	49.844
	Reputation (R)	R1:0.805; R2:0.741; R3:0.747; R4:0.648; R5:0.702	0.777	53.391
	Communication (C)	C1:0.725; C2:0.662; C3:0.686; C4:0.706	0.740	48.322
Dependent variables	Patients’ usage willingness (P)	P1:0.732; P2:0.791; P3:0.682; P4:0.596; P5:0.738	0.752	50.546
Moderating variables	Physicians’ behavior perceived uncertainty (PU)	PU1:0.801; PU2:0.827; PU3:0.798; PU4:0.864; PU5:0.830; PU6:0.568; PU7:0.777; PU8:0.830	0.760	45.632
Moderating variables	Website uncertainty (WU)	WU1:0.734; WU2:0.674; WU3:0.607; WU4:0.628; WU5:0.649; WU6:0.607; WU7:0.635; WU8:0.762; WU9:0.708; WU10:0.554	0.850	67.140

Data collection

Data were collected via three channels: (1) the Sojump platform, the largest online survey platform in China; (2) popular forums, i.e. QQ and WeChat groups; and (3) distributing questionnaires at major hospitals in Beijing, the center of medical services in China. To stimulate participation, we gave each respondent a gift worth USD 3. In addition, 10 respondents were randomly selected to receive a cash prize of $25. Data were collected from June 30 to 31 July 2019.

Data were collected using snowball sampling, a method of gathering information through a network of personal relationships.³⁰ Online healthcare services are not as mature as traditional offline medical services in contemporary Chinese healthcare; thus, only a small portion of patients currently uses the service for medical consultation. Revealing hidden users is an important feature of snowball sampling. By surveying relevant participants, data can be retrieved from a small to a large group relevant to our topic.

Finally, 600 questionnaires were distributed, of which 565 were effectively recovered: 196 from the Sojump platform, 159 from online forums, and 210 from offline surveys. Ultimately, 133 questionnaires were excluded because of a high rate of identical answers for all items, resulting in a final sample size of 432. Table 5 summarizes respondents’ demographic information.

Table 5.

Demographic information.

Variables	Category	Respondent
Age	<18	2
	18–22	146
	23–35	211
	36–50	68
	>50	5
Gender	Male	181
Gender	Female	251
Education	Primary school	2
	Junior high school	8
	High school or technical secondary school	34
	Bachelor’s or junior college degree	266
	Postgraduate and above	122
Online healthcare platform	www.haodf.com	289
	www.39.net	44
	Others	99

First, there were 251 women (58.10%) and 181 men (41.90%). This is in line with previous research that women had more online shopping experiences than men.³¹ Second, our survey subjects were mostly aged between 18 and 50 years. In online shopping, users born in the 1990s accounted for 50.0%, those born in the 1980s for 36.1%, and those born between the 1950s and 1970s for 13.9%.³² Under 18 years old was 0.46%, 18–22 years old was 33.80%, 23–35 years old was 48.84%, 36–50 years old was 15.74%, and above 50 years old was 1.16%, so the age of users in the data set is mainly concentrated in 18–35 years old (82.64%), which fit the age distribution of online shopping users already known. Third, 89.81% of all respondents have a college degree or above, which is consistent with the characteristics of the population engaging in online shopping in China. Previous studies evidences that 77.0% of users in online shopping have a bachelor’s degree or higher.³² Highly educated users will be more willing to accept new things and use the Internet more skillfully. Overall, these features indicate that the data set is representative.

Results

Table 6 presents the results for the correlation coefficient, mean, and standard deviations.

Table 6.

Correlation analysis.

Variables	1	2	3	4	5	6	7	8	9	10
Patients’ usage willingness (1)	1	—	—	—	—	—	—	—	—	—
Supervision (2)	0.668^**	1	—	—	—	—	—	—	—	—
Reputation (3)	0.693^**	0.763**	1	—	—	—	—	—	—	—
Communication (4)	0.655^**	0.690**	0.720**	1	—	—	—	—	—	—
Physicians’ behavior perceived uncertainty (5)	0.505^**	0.520**	0.578**	0.535**	1	—	—	—	—	—
Website uncertainty (6)	0.050	0.051	0.005	0.068	−0.012	1	—	—	—	—
Sex (7)	−0.025	−0.078	−0.083	−0.058	−0.060	0.119*	1	—	—	—
Age (8)	0.049	0.086	0.071	0.092	0.052	0.158**	−0.182**	1	—	—
Income (9)	−0.072	−0.066	−0.096*	−0.077	−0.034	0.199**	−0.148**	0.247**	1	—
Network age (10)	0.002	0.025	0.014	0.024	0.030	0.149**	−0.065	0.297**	0.229**	1

Note: N = 432; ** for p < 0.010, * for p < 0.050.

SPSS 20.0 statistical software was used to test the hypothesis of the main and regulatory effects using the hierarchical regression method (HRM). To avoid the problem of multiple collinearity caused by adding interactive terms, the independent and regulatory variables were first centralized and then multiplied to construct the interaction term. The variance expansion coefficients (VIF) of the regression analysis in Model 6 were all less than 7, lower than the standard value of 10, indicating that the problem of multicollinearity among explanatory variables is not serious. The Durbin-Watson test value is 1.896, indicating no autocorrelation between the residuals of explanatory variables. Table 7 shows the regression analysis.

Table 7.

Regression analysis.

	Variables	Patients’ usage willingness					VIF
	Variables	Model 1	Model 2	Model 3	Model 4	Model 5	VIF
Control variables	Sex	0.035	0.033	0.025	0.030	0.023	1.041
Control variables	Network age	−0.013	−0.017	−0.010	−0.016	−0.010	1.036
Independent variables	Supervision (S)	0.252***	0.240***	0.212***	0.237***	0.211***	2.749
	Reputation (R)	0.322***	0.295***	0.362***	0.315***	0.372***	3.251
	Communication (C)	0.251***	0.230***	0.289***	0.224***	0.283***	2.533
Moderating variables	Physicians’ behavior perceived uncertainty (PU)	—	0.090**	0.101**	0.083**	0.095**	1.624
Moderating variables	Website uncertainty (WU)	—	0.020	0.019	0.025	0.024	1.123
PInteraction terms	S × PU	—	—	0.142**		0.131**	5.184
	R × PU	—	—	0.200**		0.176**	5.477
	C × PU	—	—	0.153**		0.155**	5.004
	S × WU	—	—	—	−0.122**	−0.104**	3.458
	R × WU	—	—	—	−0.142	−0.110	3.794
	C × WU	—	—	—	−0.051	−0.038	2.900
	R ²	0.556	0.561	0.602	0.572	0.608	D-W = 1.896
	Adjusted R ²	0.550	0.554	0.593	0.562	0.596
	F Value	106.494***	77.366***	63.685***	56.297***	49.893***
	ΔR ²	0.555	0.005	0.041	0.011	0.047

Note: *** for p < 0.001, ** for p < 0.010, * for p < 0.050.

The first step was to add three independent variables: supervision, reputation, and communication (Model 1). The regression results showed that the model was valid (R² = 0.556, F = 106.494, p < 0.001). The effect of the supervision mechanism on online patients’ usage willingness was significant (β = 0.252, p < 0.001), verifying H1, while the reputation mechanism had a significant effect on online patients’ usage willingness (β = 0.322, p < 0.001), confirming H2. The communication mechanism had a significant effect on online patients’ usage willingness (β = 0.251, p < 0.001), verifying H3.

In the second step, the moderating variables (physicians’ behavior perceived uncertainty and website perceived uncertainty) were added. The regression results showed that Model 2 was valid (R² = 0.561, F = 77.366, p < 0.001). The effect of doctors’ behavioral perceived uncertainty on online patients’ usage willingness was significant (β = 0.090, p < 0.05), whereas the effect of website perceived uncertainty on online patients’ usage willingness was not significant (β = 0.020, p > 0.05).

The third step was adding the interaction terms of the physicians’ behavior perceived uncertainty and three independent variables (Model 3) based on Model 2. The regression results confirmed the model is valid (R² = 0.602, F = 63.685, p < 0.001). Physicians’ perceived uncertainty significantly regulated the relationship between the supervision, reputation, and communication mechanisms, and online patients’ usage willingness (β = 0.142, p < 0.05; β = 0.200, p < 0.05; β = 0.153, p < 0.05), and was still significant in Model 5. Figures 2–4 further show the regulatory model of physicians’ behavioral perceived uncertainty. When physicians’ behavioral perceived uncertainty is high, the positive impact of the supervision, reputation, and communication mechanisms on online patients’ usage willingness is stronger, confirming H4, H5, and H6.

Figure 2.

Interaction between supervision mechanism and physicians’ behavior perceived uncertainty.

Figure 3.

Interaction between reputation mechanism and physicians’ behavior perceived uncertainty.

Figure 4.

Interaction between communication mechanism and physicians’ behavior perceived uncertainty.

The fourth step was adding the interaction term of website perceived uncertainty and three independent variables (Model 4) based on the second step. The regression results confirmed the model is valid (R² = 0.572, F = 56.297, p < 0.001). The perceived uncertainty of the website can significantly regulate the relationship between the supervision mechanism, and online patients’ usage willingness (β = −0.122, p < 0.05), verifying H7, and was still significant in Model 5 with a certain degree of robustness. Perceived uncertainty of the website had no significant effect on the relationship between the reputation, communication mechanism and online patients’ usage willingness (β = −0.142, p < 0.05; β = −0.051, p < 0.05), so H8 and H9 are not supported. Figure 5 show the regulatory mode of perceived website uncertainty. Figure 5 shows that when the degree of website uncertainty increases, the influence of the supervision mechanism on online patients’ usage willingness changes from a positive to negative correlation.

Figure 5.

Interaction between supervision mechanism and website uncertainty.

Discussion

Principal findings

This study draws the following conclusions: First, the supervision mechanism can effectively enhance online patients’ willingness to use web-based medical platforms. The uncertainty of physicians’ behavioral uncertainty perception positively moderates the relationship between the monitoring mechanism and patients’ use intention. The effect is stronger when the uncertainty perception of physicians’ behavior is high. Furthermore, when patients perceive uncertainty regarding physicians, they rely more on the supervision mechanism of medical websites. Web-based medical platforms can be considered third parties for both physicians and patients. When the degree of website uncertainty increases, the influence of the supervision mechanism on online patients’ usage willingness changes from a positive to negative correlation, indicating that when patients face low website uncertainty, the better the supervision mechanism, the stronger is online patients’ purchase willingness. However, when patients face high website uncertainty, the better the supervision mechanism, the weaker is online patients’ purchase willingness. Thus, for web-based platforms, when doctors’ behavior perceived uncertainty is high, the supervision of doctors’ behavior and the services they provide should be strengthened; On the contrary, when website uncertainty is high, the supervision mechanism appropriately should be weaken, and give full play to the role of the market regulation.

Second, the reputation mechanism positively impacted online patients’ usage willingness. Earlier studies explored online reputation in the context of e-commerce and online auction markets, suggesting the usefulness of user-generated content and online reputation feedback.³³ Online reputation is positively correlated with patients’ choice of online physician.³⁴ In addition, when doctors’ behavior uncertainty perception is high, patients rely more on the credit evaluation system to make decisions. The online reputation system helps patients make decisions when choosing a doctor while monitoring and improving the quality of doctor services.³⁵ But regardless of whether the perceived uncertainty of the website is high or low, there is no significant change in the effect of the reputation mechanism on online patients’ usage willingness. Thus, web-based platforms should pay attention to the construction of reputation mechanism with the high doctors’ behavior perceived uncertainty, so that the reputation mechanism fully reflects the service quality level of doctors; in addition, when website uncertainty is high, web-based platforms should first consider rebuilding its reputation and increasing the trust of users.

Third, the communication mechanism can effectively enhance online patients’ usage willingness. Customer perceptions and buying behaviors are influenced by interactions with employees.³⁶ In addition, physicians’ behavioral uncertainty perception positively moderates the relationship between the communication mechanism and patients’ use intention. Enterprises should encourage employees and promote customer satisfaction and purchase behavior.³⁷ Online health consultation platforms must stimulate communication between doctors and patients to eliminate mistrust and establish purchase intention. However, regardless of whether the perceived uncertainty of the website is high or low, there is no significant change in the effect of the communication mechanism on online patients’ usage willingness. Possibly, when the perceived uncertainty of the website is low (the website has a good reputation), the communication function of the medical platform is not perfect enough to realize fast and smooth communication between patients and doctors, and the information asymmetry between the two sides of the transaction cannot be effectively alleviated. Thus, when doctors’ behavior perceived uncertainty is high, web-based platforms should establish easy-to-use and efficient communication software to alleviate the information asymmetry between doctors and patients; when website uncertainty is high, the construction of communication mechanism is not so urgent, but to do a good job of crisis public relations, enhance the credibility of the website.

Limitations

This study has produced some beneficial results; however, there are still certain limitations that must be improved future studies. First, the moderating effect of website uncertainty on the relationship between reputation mechanism, communication mechanism and patients’ purchase intention has not been verified. Future studies will consider adding other governance mechanisms, such as privacy protection mechanism and transaction security mechanism, etc. Second, the moderating effect of website uncertainty on the relationship between reputation mechanism and patients’ purchase intention has not been proved. Future research should delineate positive and negative patient evaluations in the reputation mechanism to investigate the effect of these different evaluations on online patients’ usage willingness. Third, the moderating effect of website uncertainty on the relationship between communication mechanism and patients’ purchase intention has not been significant. In the future, communication mechanism can be divided into paid communication and unpaid communication for analysis. Fourth, this study only examined the governance of private platforms on the opportunistic behavior of third-party sellers, and did not consider platforms directly operated by public hospitals. In the future, how to govern such websites to rationally use medical resources should be investigated.

Conclusions

Based on transaction cost theory and social exchange theory, this paper not only empirically tests the positive influence of supervision mechanism, reputation mechanism and communication mechanism on patients’ willingness to use, but also further tests the moderating effect of perceived uncertainty of website and physicians’ perception on this influence. This paper draws the following three conclusions. Firstly, the supervision mechanism has a positive effect on patients’ use intention when doctors’ perceived uncertainty is high. However, website uncertainty negatively regulates the influence of the supervision mechanism on the patients’ willingness to use, which indicates that when the degree of website uncertainty increases, the supervision mechanism decrease the patients’ willingness to use. It may be that when the website has a poor reputation (high website uncertainty), the platform cannot effectively solve the disputes between doctors and patients, which leads to the decrease of patients’ perception of quality, and then affects patients’ purchase intention. Secondly, reputation mechanism has a positive effect on patients’ purchase intention only when doctors’ perceived uncertainty is high. The effect is stronger when physicians’ behavior perceived uncertainty is high. It shows that patients value their doctors’ online reputation. Thirdly, communication mechanism has a positive effect on patients’ purchase intention when doctors’ behavioral perceived uncertainty is high. This may be because when doctors’ behavioral perceived uncertainty is high, patients can determine whether the doctor’s service meets their expectations through adequate communication with doctors, and the information asymmetry between the two sides of the transaction is effectively alleviated, and the purchase intention of patients is improved.

It is worth noting that the moderating effect of website uncertainty on the relationship between reputation mechanism, communication mechanism and patients’ purchase intention has not been verified. Possible explanation is that when poor website credibility (higher website uncertainty), regardless of the platform’s reputation mechanism and communication mechanism construction of perfect or not, patients are that the system is unable to play an important role.

Web-based consultation platforms can selectively strengthen the corresponding governance mechanism according to the uncertainty conditions, so as to improve the purchase intention of patients. The conclusions of this study have the following implications for the management of web-based consultation platforms. (1) When doctors’ behavior perceived uncertainty is high, attention should be paid to the supervision mechanism, such as investing funds to strengthen the technical monitoring of doctors’ behavior and their services provided, strengthening the training of customer service staff, standardizing the process of online complaint mechanism, and improving the efficiency of complaint acceptance. At the same time, we should also pay attention to the reputation mechanism, such as improving the credit evaluation system, intensifying the crackdown on “click farming” and encouraging patients to fill in the evaluation. Establish convenient communication mechanisms, such as improving the ease of use of online communication software. (2) Compared with the supervision mechanism and communication mechanism, when doctors’ behavior perceived uncertainty is low, attention should be paid to the communication mechanism, such as strengthening the technical investment in online communication system and developing communication software. (3) When the website uncertainty is low, we should strengthen the construction of supervision mechanism; When the website perception uncertainty is high, strengthening the supervision mechanism will reduce patients’ purchase intention instead.

Footnotes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 71672008 and 71972012) and the Key Project of the Major Research Plan of the National Natural Science Foundation of China (Grant No. 91746210).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was supported by the The Key Project of the Major Research Plan of the National Natural Science Foundation of China;91746210,10.13039/501100001809;National Natural Science Foundation of China;71672008 and 71972012.

Ethics approval

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975,as revised in 2013.

Informed consent

Each respondent completed voluntarily the questionnaire after being informed. Informed consent was obtained from all individual respondents included in the study.

ORCID iD

Dixuan Ren

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