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The relationship between patient donation and satisfaction with online healthcare service quality.

The worldwide online healthcare industry has experienced rapid expansion in recent years. As one of the biggest in the world, the Chinese online healthcare market has increased by more than 50% since 2016 and earnings exceeded 12 billion RMB (approximately US$1.7 billion) in 2017 (Electrical Design News, 2018). Using computer applications (apps) to obtain health information has become increasingly popular among Chinese smartphone users.

To encourage doctors to provide high-quality service online, the Chinese mobile healthcare platforms have adopted donation mechanisms similar to those used on the WeChat and Douyin social networking platforms. The mechanism allows patients to donate virtual gifts, for example, flowers and flags, to online physicians, and the revenue generated from donation is shared by the physician and the owners of the platform, providing financial reward for both. More important, patient donations could be a signal of excellent quality and, thus, could enhance the social reputation of both the physician and the platform.

Making donations has long been studied as a charitable behavior in research on psychology (Boenigk & Helmig, 2013), economics (Krishnamurthy & Tripathi, 2009), and nonprofit organizations (Bekkers & Wiepking, 2010). Sargeant, Ford, and West (2006) concluded that the motivation for donation is intrinsically associated with empathy and sympathy. Recently, researchers have begun to focus on the consumptive attribute of donation behavior in the context of social media platforms. Wan, Lu, Wang, and Zhao (2017) viewed donation as the donor's payment for the consumption of the knowledge or service. Wallace, Buil, and de Chernatony (2017) considered that donation on social media is a form of conspicuous consumption. Compared to donation to a charity and donation on social media, patient donation to an online healthcare service has some unique features. First, the patient as a donor is the person who needs the physician's help, whereas the charitable donor generally is the person who helps others (Ye, Teng, Yu, & Wang, 2015). Thus, patient donation should not be regarded as a charitable behavior. Second, most patients want to keep their illness situation private, so patient donation is unlikely to be a conspicuous consumption behavior. Therefore, we considered patient donation as a purely consumption behavior, that is, the payment for medical knowledge or suggestions online from a physician. Considering that the user's willingness to pay (WTP) increases incrementally as the degree of satisfaction increases (Homburg, Koschate, & Hoyer, 2005), the degree of patient satisfaction with an online healthcare service might be an important factor influencing the possibility that the patient will make a donation.

On the basis of the above arguments, in this study we explored the relationship between patient donation and patient satisfaction with the online healthcare service. We focused on two types of patient satisfaction: satisfaction with the process quality and satisfaction with the outcome quality, and investigated how these affect patient donation. Given that both free users and paying users can donate to physicians, we also investigated the difference in donation behavior between these two types of patient. Therefore, our research questions were as follows:

Research Question 1: How is patient donation behavior affected by satisfaction with the quality of process and of outcome of using of an online healthcare service?

Research Question 2: What is the difference in donation behavior between free and paying patients in use of an online healthcare service?

By answering these questions, we hoped to extend understanding of donation behavior from the context of traditional and social media settings to the setting of online healthcare.

Literature Review and Hypothesis Development

Donation Behavior and Satisfaction With Service Quality

For a long time, donation was studied as a charitable behavior (Bekkers & Wiepking, 2010; Sargeant et al., 2006). Savary, Goldsmith, and Dhar (2015) considered donation as an act to self-signal an individual's personal traits, such as being sympathetic and unselfish. Wan et al. (2017) proposed that donation on social media platforms also has a consumption attribute. Using a dataset of 303 users on YY platform, which is a popular user-generated live-streaming video platform in China, in the same category as, they found that users' donation intention relates to their perceived value of the service. Thus, donation may depend on users' assessment of and degree of satisfaction with service quality. Despite the lack of research in which the relationship of consumer satisfaction with service quality and donation has been directly investigated, scholars have provided evidence of the positive impact of customer satisfaction with service quality on customers' WTP (Homburg et al., 2005) and future repurchase intention (Chou & Hsu, 2016; Ramamoorthy, Gunasekaran, Roy, Rai, & Senthilkumar, 2018). Thus, we reasoned that there might be a connection between patient donation and online healthcare service quality satisfaction.

Generally, service quality has been measured in terms of three dimensions (Lu, Zhang, & Wang, 2009): behavior, attitude, and expertise (process quality); physical equipment and ambient conditions (environment quality); and waiting time and valence (outcome quality). Taking as a consideration that the environment factors are less important in the e-service setting, Chen and Kao (2010) simplified the measurement of e-service quality to the two dimensions of process quality and outcome quality. On this basis, Chou and Hsu (2016) examined how process quality satisfaction and outcome quality satisfaction affect consumers' repurchase intention in the online environment, and found that both types of satisfaction positively affect consumer future WTP online. Thus, we hypothesized that patients' satisfaction with outcome quality and process quality would increase their WTP in the context of online healthcare; hence, satisfaction would increase the chance of patient donation. Our hypotheses were as follows:

Hypothesis 1: Satisfaction with outcome quality of an online healthcare service will be positively related to patients' donation decision.

Hypothesis 2: Satisfaction with process quality of an online healthcare service will be positively related to patients' donation decision.

Donation Behavior of Free and Paying Patients

Besides the altruistic motivation of donation associated with empathy and sympathy (Sargeant et al., 2006), Krishnamurthy and Tripathi (2009) proposed the reciprocity motivation of donation. They found that people who had previously received help or donation from others were more likely to donate. On online healthcare platforms, physicians provide both free and paid consultation services to patients. The paid service is for profit and the patient must pay before the service begins. In contrast, the free service is nonprofit. Given the same satisfaction degree for both types of service, there may be a stronger possibility that patients who choose to use the free service will donate because of the stronger reciprocity motivation. Thus, we formed the following hypothesis:

Hypothesis 3: Free patients will be more likely to donate to an online healthcare service than will paying patients.


Participants and Procedure

We collected the research data from the ChunyuDoctor app, which is one of the largest mobile healthcare platforms in China. By the end of 2015, ChunyuDoctor had nearly 200,000 registered doctors and 65 million registered patients (Y. Hu, 2019). On the platform patients are able to submit questions and upload files with laboratory results and images to physicians. The consultation can be either free or paid for, according to the patients' choice. After the consultation, patients are requested to submit feedback to assess the service by clicking an existing text tag or creating a new tag. Additionally, they can also decide whether to donate virtual gifts to their physician. To protect patient privacy, all personal patient information is removed and their medical profiles are anonymized on ChunyuDoctor before, for the purpose of healthcare knowledge sharing, the contents of questions and answers between patient and physician are made publicly available. Commercial use of these exchanges is prohibited. We collected the consultation contents from the app using a Java-based web crawler and stored the data in an ACCESS database in December 2016. The final sample consisted of 496,723 consultation records provided by 25,933 physicians. Table 1 shows the information in the consultation records we collected.

In our dataset, patients made a donation after about 4.24% of the consultations (see Table 2); thus, the total ratio of donations was very low. However, given that the paid consultation service on the platform accounts for only 7.7% of the total, the donations could also be a vital means for physicians to gain revenue. Table 2 also shows that only 3.76% of free patients donated, in contrast to 9.45% of paying patients.

Variables and Measures

Patients' donations. To address our research questions, we constructed a dummy variable, Is_Donation, as the dependent variable to represent whether the patient donates after the consultation. If a patient makes a donation, Is_Donation = 1, otherwise Is_Donation = 0.

Satisfaction with outcome quality and process quality. We identified each patient's level of satisfaction with outcome and process quality from the feedback tags. In our dataset there were 16 types of feedback tag, as illustrated in Table 3.

Waiting time was too long ([phrase omitted])

These tags represent the patients' satisfaction with the process quality dimensions (attitude and inquiry during the physician--patient interaction) and with the outcome quality dimensions (information usefulness and the response time), as listed in Table 4. Thus, we constructed four variables to denote patients' satisfaction: attitude, inquiry, response, and usefulness.

According to Gao, Greenwood, Agarwal, and McCullough (2015) and N. Hu, Koh, and Reddy (2014), customers have a stronger intention to comment when they feel either particularly dissatisfied or particularly satisfied. Otherwise, they tend to keep silent. Thus, a positive feedback tag indicates the highest satisfaction level, a negative feedback tag indicates the lowest satisfaction level, and the lack of a feedback tag indicates midlevel satisfaction. Taking a response as an example, if the patient submits a negative tag such as "Hope the physician can reply faster," we assessed the patient's satisfaction with the response at the lowest level, so the score for the response was 0. In contrast, if a positive tag such as "Timely reply" was submitted, we assessed the patient's satisfaction at the highest level and the response score was 2. When patients did not submit any tags to comment on the quality of the physician's response, the score for the response was 1. Moreover, we created a dummy variable, paid, to denote whether the service was free (score = 0) or provided for a fee (score = 1).

Control Variables

To control for the impact of the physician's professional title and the hospital brand, we introduced three dummy variables: senior, big city, and tertiary hospital. For a chief physician or associate chief physician, senior = 1; otherwise senior = 0. If the physician's hospital was located in a provincial capital or municipality, big city = 1; otherwise, big city = 0. For a tertiary hospital, which is a comprehensive or general hospital serving as a medical hub for multiple regions to provide specialist health services, and which performs a bigger role than other hospitals do with regard to medical education and scientific research, tertiary hospital = 1; otherwise, tertiary hospital = 0. The number of replies sent by the physician during the consultation was identified as reply and this variable was used to control for the impact of the differing levels of physicians' efforts. In addition, when the consultation was finished, the app may be programmed to send a gift-donation notification to patients. The notification may increase the chance of a donation. Thus, we generated a variable we labelled as promotion, to control for the promotion effects of the notification. In Table 5 the descriptive statistics of the key variables are presented.

Research Model

To address the research questions, we developed a multiple probit regression model:

Probit (IS_Donation)

= [alpha] + [[theta].sub.1]Useful + [[theta].sub.2]Response + [[delta].sub.1]Inquiry + [[delta].sub.2]Attitude + [[beta].sub.1]Paid + [[beta].sub.2]logReply + [[beta].sub.3]Big city + [[beta].sub.4]Senior + [[beta].sub.5]Tertiary + [[beta].sub.6]Promotion + [[gamma].sub.7]Year 2016 + [[tau].sub.g][summation][D.sub.g] + [epsilon] (1)

In our model, [[theta].sub.1] and [[theta].sub.2] capture the effect of outcome quality satisfaction on patients' donation behavior, and [[delta].sub.1] and [[delta].sub.2] capture the impact of process quality satisfaction. The coefficient [[beta].sub.1] captures the difference in donation behavior between free and paying patients. We also included a year dummy variable and a set of department dummy variables ([D.sub.g]) in our model to control for department differences and the impact of time-related factors. Reply was transferred into logarithmic form to eliminate estimation bias.


Impact of Outcome Quality Satisfaction and Process Quality Satisfaction

We first estimated our model using all samples of both free and paying patients. The main results are reported in column 1 of Table 6. The coefficients of both usefulness and response were positive and significant, indicating that patient satisfaction with these factors led to an 8.2% and 13% increase, respectively, in the chance of donation. Thus, Hypothesis 1 was supported.

Furthermore, the chance of donation increased by 15.6% if the patient felt satisfied with the process of the inquiry (supporting Hypothesis 2). However, the influence of attitude was positive but not statistically significant, suggesting that the physician's attitude is not the main factor affecting patients' donation behavior. Each of the control variables of [log.sub.reply], promotion, senior, and tertiary hospital had a positive and significant impact on patient donation behavior.

Difference Between Free and Paying Patients

As shown in column 1 of Table 6, the coefficient of the variable paid was positive (p < .01), indicating that paying patients were more likely to donate than were free patients; therefore, Hypothesis 3 was not supported. This result might be attributable to differences in the quality of the consultation service that physicians provide to the two groups. Figure 1 depicts the distribution of feedback tags of free and paying patients in our dataset. Of the paying patients, 48%-55% made a positive evaluation of the physician's service quality, whereas the ratio for the free patients was 37%-40%.

We further investigated the differences between the paying and free patient groups in regard to the impact of their satisfaction on making a donation. Column 1 results in the model were estimated using both the paying and free patient groups, with results reported separately for each group in columns 2 and 3 of Table 6. For the paying patients, the coefficient of inquiry was larger than those of usefulness and response ([[delta].sub.1] > [[theta].sub.1] + [[theta].sub.2]), suggesting that satisfaction with process quality had a much greater impact on patient donation than did satisfaction with outcome quality. For the free patients, the impact on donation was equal for inquiry and response ([[delta].sub.1] = [[delta],.sub.2] suggesting that the effect of both types of satisfaction was equally important in enhancing the possibility of the patient making a donation.


Key Findings

In this study we explored the relationship between patient donation and satisfaction with online healthcare service quality. Our results show that patients' donation was positively related to two types of quality satisfaction: process quality and outcome quality. Furthermore, the results indicate that the possibility of the patient making a donation was higher for paying patients than for free patients. This could partly be caused by a perception of difference in service quality between the two groups. We also found that satisfaction with process quality, particularly the physician's careful inquiry about patient history and symptoms, was of vital importance to enhance the possibility of patient donation, especially for paying patients.


In prior research on donation behavior the main focus has been on charity settings (Savary et al., 2015; Ye et al., 2015), in the online community (Krishnamurthy & Tripathi, 2009), and on social media platforms (Wan et al., 2017). Our main contribution to the literature is that we have extended the understanding of donation behavior to the online healthcare setting. We have also distinguished satisfaction with process quality from satisfaction with outcome quality when examining the impact of patients' satisfaction on their donation behavior. Our study findings show that both types of satisfaction have a positive impact on patient donation. These findings are consistent with those of prior researchers who found that satisfaction increases consumer WTP (Homburg et al., 2005) and purchase intention (Chou & Hsu, 2016). Thus, the findings provide a reference for future research on the link between satisfaction and donation.

Our findings also have practical implications for physicians and platform managers. Given that the two types of quality satisfaction we examined jointly influenced patient donation, to increase the likelihood of patient donation and/or future purchasing, physicians should focus on both outcome and process quality of online healthcare services, especially the inquiry process relating to patient history and symptoms. Platform managers should also make efforts to ensure all patient enquiries are answered promptly. These joint efforts could enhance patient satisfaction and increase patient donation or purchasing intention on the platform.


One limitation of this research is that we could not obtain demographic information of each patient, such as age and gender, because of the privacy policy of the ChunyuDoctor app. Moreover, only a few patients in our sample submitted negative reports, which may have biased our regression results. Further research using datasets with more detailed demographic information could provide deeper insight into patient donation to and satisfaction with online healthcare services.


The main objective of this study was to explore how patients' donation behavior is related to their satisfaction with an online healthcare service. The empirical results reveal that satisfaction with outcome quality and satisfaction with process quality jointly influenced patient donation. We also found that paying patients were more likely to donate than were free patients. The results have generated several theoretical contributions and provide some important practical suggestions for physicians providing online services and for the mobile healthcare platforms.


This study was supported by the Youth Project of Humanities and Social Sciences of the Ministry of Education of China (18YJCZH052).


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Yuanrong Hu (1), Shengkang Lu (1), Zhongming Tang (1)

(1) School of Management, Hubei University of Education, People's Republic of China

CORRESPONDENCE Zhongming Tang, School of Management, Hubei University of Education, Gaoxin 2nd Road 129, Donghu Hi-Tech Development District, Wuhan 430074, Hubei, People's Republic of China. Email:
Table 1. Information in Consultation Records

Information in records        Description

Donation behavior             Whether the patient donates
Physician information         Department
                              Job title
                              Hospital's grade
Information on consultations  Identification number of each
                              Content of communication between
                              physician and patient
                              Time of consultation
Online feedback               Tag of patient's quality feedback

Table 2. Donation Ratios of Online Consultation Patients

                         Free patients  Paying patients  Total

Number of consultations  454,463        42,260           496,723
Number of donations       17,103         3,993            21,096
Ratio of donations             3.76%         9.45%             4.24%

Table 3. Quality Tags Used by Patients

Positive quality tags                 Negative quality tags

Very clear answer ([phrase omitted])  Hope the physician can reply
                                      faster ([phrase omitted])
Very careful ([phrase omitted])       Hope the physician can be more
                                      zealous ([phrase omitted])
Very helpful ([phrase omitted])       Unprofessional ([phrase omitted])
Timely reply ([phrase omitted])       Not careful ([phrase omitted])
Dedicated and responsible             No help ([phrase omitted])
([phrase omitted])
Very friendly ([phrase omitted])      Needs to be more careful
                                      ([phrase omitted])
                                      Waiting time was too long
                                      ([phrase omitted])
                                      Answer should be clearer
                                      ([phrase omitted])
                                      Cannot understand
                                      ([phrase omitted])
                                      Unfriendly ([phrase omitted])

Table 4. Types of Satisfaction and Indicators

Dimension          Indicators  Positive tags

Satisfaction with  Attitude    Very friendly
process quality
                   Inquiry     Very careful
                               Dedicated and responsible
Satisfaction with  Response    Timely reply
outcome quality
                   Usefulness  Very clear answer
                               Very helpful

Dimension          Negative tags

Satisfaction with  Unfriendly
process quality    Hope the physician can be more zealous
                   Needs to be more careful
                   Not careful
Satisfaction with  Hope the physician can reply faster
outcome quality    Waiting time was too long
                   Answer should be clearer
                   No help
                   Cannot understand

Table 5. Descriptive Statistics of Key Variables

Variable                             N        M      SD     Min.  Max.

Dependent variable
  Is_Donation                        496,723  0.042  0.202  0       1
Independent variable
  Satisfaction with outcome quality
    Usefulness                       496,723  1.350  0.526  0       2
    Response                         496,723  1.372  0.517  0       2
  Satisfaction with process quality
    Inquiry                          496,723  1.347  0.520  0       2
    Attitude                         496,723  1.373  0.506  0       2
  Control variable
    Paid                             496,723  0.085  0.279  0       1
    Reply                            496,723  8.169  7.235  0     339
    Promotion                        496,723  0.377  0.485  0       1
    Big city                         496,723  0.296  0.457  0       1
    Tertiary hospital                496,723  0.473  0.499  0       1
    Senior                           496,723  0.131  0.338  0       1

Table 6. Estimation Results of the Model

                   1                   2
                   All patients        Paying patients
Variable           Is_donation

Usefulness                .0820 (***)        .0992 (***)
                        (0.00973)          (0.0246)
Response                  .130 (***)         .0870 (***)
                        (0.0101)           (0.0245)
Inquiry                   .156 (***)         .242 (***)
                        (0.00968)          (0.0246)
Attitude                 -.00107            -.0342
                        (0.0109)           (0.0272)
Promotion                 .497 (***)         .497 (***)
                        (0.00862)          (0.0221)
Paid                      .286 (***)       --
                        (0.0104)           --
Reply                     .305 (***)         .146 (***)
                        (0.00501)          (0.0111)
Big city                  .00139            -.0102
                        (0.00813)          (0.0200)
Tertiary hospital         .0145 (*)         -.00729
                        (0.00763)          (0.0242)
Senior                    .0473 (***)        .0274
                        (0.00976)          (0.0202)
N                  496,720             42,260
[R.sup.2]                 .105               .069

                   Free patients

Usefulness                .0779 (***)
Response                  .139 (***)
Inquiry                   .139 (***)
Attitude                  .00449
Promotion                 .496 (***)
Paid                    --
Reply                     .346 (***)
Big city                  .00415
Tertiary hospital         .0172 (**)
Senior                    .0484 (***)
N                  454,460
[R.sup.2]                 .101

Note. Standard errors are shown in parentheses. Three samples were
deleted from the regression analysis because of missing information for
the hospital.

(*) p < .01, (**) p < .05, (***) p < .001.

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Author:Hu, Yuanrong; Lu, Shengkang; Tang, Zhongming
Publication:Social Behavior and Personality: An International Journal
Geographic Code:9CHIN
Date:Dec 1, 2019
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