Online written consultation, telephone consultation and offline appointment: An examination of the channel effect in online health communities

Accepted Manuscript Title: Online Written Consultation, Telephone Consultation and Offline Appointment: An Examination of the Channel Effect in Online Health Communities Authors: Hong Wu, Naiji Lu PII: DOI: Reference:

S1386-5056(17)30217-4 http://dx.doi.org/10.1016/j.ijmedinf.2017.08.009 IJB 3548

To appear in:

International Journal of Medical Informatics

Received date: Revised date: Accepted date:

3-5-2017 10-8-2017 24-8-2017

Please cite this article as: Hong Wu, Naiji Lu, Online Written Consultation, Telephone Consultation and Offline Appointment: An Examination of the Channel Effect in Online Health Communities, International Journal of Medical Informaticshttp://dx.doi.org/10.1016/j.ijmedinf.2017.08.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Online Written Consultation, Telephone Consultation and Offline Appointment: An Examination of the Channel Effect in Online Health Communities

Hong Wua, Naiji Lua,* Institutions: aSchool of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Corresponding author: Naiji Lu, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Qiaokou District, Wuhan, Hubei province, China Telephone: +86-131-6006-1187 Email: [email protected]

HIGHLIGHTS

1. We research how doctor services provided via online channels impact their performance offline. Do channels substitute or complement one another? 2. We also investigate the role of reputation in influencing doctors’ performance offline. 3. We test how the channel effects change with doctors’ online and offline reputation.

Abstract Introduction: The emergence of online health communities broadens and diversifies channels for patient-doctor interaction. Given limited medical resources, online health communities aim to provide

better treatment by decreasing medical costs, making full use of available resources and providing more diverse channels for patients. Objectives: This research examines how online channel usage affects offline channels, i.e., “Online Booking, Service in Hospitals” (OBSH), and how the channel effects change with doctors’ online and offline reputation. Methods: The study uses data of 4,254 doctors from a Chinese online health community. Results: Our findings demonstrate a strong relationship between online health communities and offline hospital communication with an important moderating role for reputation. There are significant channel effects, wherein written consultation complements OBSH (β=3.320, p<0.10), but telephone consultation can be a readily substitute for OBSH (β=-9.854, p<0.001). We also find that doctors with higher online and offline reputations can attract more patients to use the OBSH (βonline=0.433, p<0.001; βoffline=2.318&2.123, p<0.001). Third, channel effects fluctuate, relative to doctors’ online and offline reputations: doctors with higher online reputations mitigate substitution effects between telephone consultation and OBSH (β=0.064, p<0.01), and doctors with higher offline reputations mitigate complementary effects between written consultation and OBSH (β=-1.586&-1.417, p<0.001). Conclusions: This study contributes to both knowledge and practice. This study shows that there is channel effect in healthcare, websites’ managers can encourage physicians to provide online services, especially for these physicians who do not have enough patients. Keywords: Online health communities; Channel effect; Online reputation; Offline reputation; Substitution/Complementation effect.

1. Introduction Today’s organizations are constantly adding new marketing channels such as the Internet in order to better serve their products and/or service receivers [1], and this phenomenon is also manifested in the healthcare industry. Despite being of universal importance, the healthcare industry is facing medical resource shortages, doctor-patient disputes, and poorly strategic utilization of medical resources problems. To solve these problems and improve services, online health communities have been emerging. According

to Health Online 2013, the number of adults who use the Internet to search online health information has increased to 59% in the U.S. [2]. A report indicates that more than 80% of patients search health information before going to a hospital in China [3]. Many patients have thus become multi-channel (online communities and offline hospital visit) users, and doctors have become multi-channel service providers. Online health communities enable doctors to better help and serve patients by providing doctors with a variety of functions. For example, by participating in online communities, doctors can publish medical articles on their own homepages to help patients access more professional medical information. Further, a Q&A service serves to help patients in solving their personalized problems online. In addition, telephone consultation and offline appointments (Online Booking, Service in Hospitals) are provided for patients who require more comprehensive consultation and treatment. Online health communities benefit both doctors and patients in that doctors can use these functions to achieve their goals more efficiently, while patients can search for health information and suggestions that can help them to recuperate faster and more effectively. With channel diversification, researchers try to find channel effects and channel choice [4, 5]. Some researchers assert that the Internet competes with traditional channels by decreasing transaction cost, such as search and monitoring costs [6-8]. For example, service receivers could find service providers in distant geographic markets who have lower prices, provide better service, offer higher quality products, or have products that better match their needs [6-12]. However, other researchers emphasize the importance of “synergies” between online and offline channels [13, 14], demonstrating that the use of multiple channels tends to be more successful. Online channels have spillover effects, generating increased purchases in offline channels [15]. However, there are only a few studies that empirically explore the channel effect. In addition, quite a few studies attempt to investigate the relationship between reputation (reviews) and performance [16-22], and they generally get consistent results that higher reputations correlate with improved performance, regardless of individual [23] or organizational performance [24]. As existing

research focuses on one channel, and both online and offline reputation are rarely examined in the same study, there are difficulties presented in ascertaining how different kinds of reputation impact on performance. Some studies show that reputation has a “premium effect” [25, 26], which can be reflected by moderating the relationship between price and purchase intentions. Because of the varying costs of different channels, certain effort to examine whether there is a moderation effect of reputation on channel effect becomes necessary. However, there is scarce existing research on channel effect that has been done using empirical methods, especially in the healthcare industry. Most research examines just one channel. Researchers also rarely study both online and offline reputation in a study, particularly how these channel effects change with reputation. Using data collected from a Chinese online health community, this study attempts to fill those research gaps. The specific research questions addressed in this paper are: RQ1: How do doctor services provided via online channels impact their performance in Online Booking Service in Hospitals (OBSH)? Do channels substitute or complement one another? RQ2: How do doctors’ online and offline reputation impact the performance of Online Booking Service in Hospitals (OBSH)? RQ3: How do channel effects change relative to the doctor’s reputation? To answer these questions, we use a dataset of 4,254 doctors providing information on a Chinese healthcare platform where doctors can advise patients in numerous forms, including online written consultation, telephone consultation and OBSH. Our data were collected from the afore-mentioned platform. We divided the channels (written consultation, telephone consultation, and appointments) into two online channels and one offline channel, according to whether the interaction between patient and doctor reached resolutions. In our study, telephone and written consultations were finished online and OBSH was completed offline. We also divided doctors’ reputation into online and offline reputation. Further, we measured performance by the appointment totals of OBSH. We used this to measure performance because the number of patients doctors treat in offline hospitals is an important performance measure, which then determines the doctors’

income. We then investigated the relationship between performance, channel effect, and reputation.

2. Literature Review 2.1 Online and Offline Channels Online marketing channels have emerged with the development of Web 2.0 (A collection of open-source, interactive and user-controlled online applications expanding the experiences, knowledge and market power of the users as participants in business and social activities [87]), which has created opportunities for product or service providers to leverage on online and offline channels in order to serve their receivers [27]. Most product or service providers have initiated their online channels to expand their existing traditional offline channels [28], and this is becoming an indispensable part of competition and survival in the market. In this way, product or service receivers gradually migrate from single channel users into multi-channel users through channel extension. During this channel extension process, product or service receivers’ experiences with a product or service provider in one channel may affect their perceptions and beliefs about the same provider in another channel [29]. Consumers display complex shopping behaviors in the emerging multi-channel environment, which includes traditional retail stores and the Internet [30, 31]. Therefore, examining the determinants of consumers’ one channel behavior is critical when considering the impact of another channel on evaluation and use. A great deal of past research has been devoted to understanding product or service receivers’ online channel adoption, believing that perceived risk is an important factor when product or service receivers consider whether to purchase online or in a brick-and-mortar store [32, 33]. Researchers have generally viewed the online channel to be isolated from the offline channel rather than as an extension of the traditional offline channel [34, 35]. Recently, a few studies have discussed the product or service receivers’ online behaviors in a multi-channel context that consider channel interactions [34, 36]. Based on the existing literature on online and offline channels, we have found a few studies that empirically explore channel effect [37, 38]. With the development of online channels, researching

determinants of consumers’ single channel behavior is critical in considering the impact of another channel on evaluation and use. Thus, the relationship between online and offline channels needs to be examined. 2.2 Online Health Communities The use of social network software with its ability to enrich the connection between patients and the rest of the medical industry has been dubbed “Health2.0,” [39, 40] and the number of organizations adopting Health 2.0 is growing. A number of online communities have been developed by patient organizations, providers, and nonprofit organizations in recent years, thus making it easier for patients to find health information [41]. Such online communities are virtual forums for patients to discuss their health concerns, share information about treatments and support, and communicate with doctors, an example of which is patientslikeme.com. Most online health communities are characterized by two

main functions: information search and social support [42]. Researchers have started to investigate the economic value (such as lower cost and increased sales) [43] and social value (such as reduced health disparities and provision of social support) [44]. Numerous studies have discussed benefits of online health communities for doctors [45] and patients [46], and these studies mainly focus on information obtained and social support. Also, they study mostly patients’ perspectives and their patient behaviors [47]. Xiao et al. [48] examined factors that influence patients’ online health information searches and found that perceived health status could affect patients’ online health search frequency and diversity. Privacy concern, trust, and information sensitivity are factors that have an impact on people’s decisions about whether or not to place their health information online [49]. Evidence about the impact of participating in online communities on medical outcomes is limited. Health intimately concerns everyone, and with the emergence of online health communities, doctors have more choices in the ways of helping patients. Based on existing literature, we have found few studies exploring the impact of online health communities on offline hospitals. Our study aims to fill this

gap. 3. Research Hypotheses Existing studies rarely research channel effect in healthcare, which is an emerging area of consideration. The studies that have concerned themselves with this topic have failed to examine how the channel effect changes with the service providers’ reputation. We extend previous research by studying the online health community, where doctors can choose from three means of servicing their patients: written consultation, telephone consultation, and OBSH. For doctors’ reputations, we divide reputation into online and offline reputation, according to the source of the reputation. We also test for the moderating effect of reputation on channel effect, which can help doctors to plan strategically in order to achieve career goals. Figure 1 shows the conceptual model of this study. The hypotheses, presented below, are established according to the relationships expressed in the model in Figure 1. Doctor Reputation Doctor Gifts (online) Doctor Title (offline) H4a-H4d

Online Written Consultation

H 3a

H

3b

H1a H1b H2a H2b

Offline OBSH Totals

Telephone Consultation

Figure 1. Conceptual Model 3.1 Online Health Community in China As a result of the limitation of existing health services, online communities in China have emerged in recent years. China has the world’s largest population and thus represents a huge resource-consumption country1. China’s large population generates a variety of unique needs relating to medical services, therefore exhibiting unique behaviors within online health communities. 1

Information can be accessed from http://drug.39.net/cpess2014/141126/4525252.html.

Haodf.com network (www.haodf.com/), an example of an online health community, helps a great many patients by providing several services: written consultation, telephone and OBSH services. These services offer their consumers different charges. Written consultations are free for the first three times, after which patients need to pay for subsequent use. In contrast, online telephone consultation is always pay-for-use, regardless of how many times you use the service. For OBSH, the platform provides this function for free, where patients can make an appointment online to see a doctor at the hospital (albeit patients have to pay for transportation to go to the hospital). Online written consultations and telephone consultations are delivered online, and OBSH is delivered offline, as the appointments are actually made in hospitals. Online written consultation models have unique attributes, as do telephone consultation and OBSH. These differences produce the disadvantages and advantages for different channels. For online written consultation, it may take patients a long time to contact doctors, because doctors are not always available via the platform. This means that every time patients make contact, they need to repeat all their details, and it may take several attempts to access a doctor. In other words, interaction is discontinuous. The advantage, however, is that when patients consult a doctor online, they can upload “check results” and pictures of affected part/s of the body, which can help the doctor to provide better advice. Similarly, as the consultation is discontinuous, doctors could ask patients to upload electronic materials based on doctors’ need to help them give more accurate advices. For telephone consultation, the advantage is that interactions are continuous, as doctors and patients can constantly interact within a period of time, between 10 to 15 minutes. The disadvantage is that patients cannot upload any electronic material to the doctor during this service. For OBSH, patients can get face-to-face treatment from doctors in the hospital. Like any e-commerce platform, information about doctors is available on the Haodf.com network, including a doctor’s title, hospital, education, work experience and patient reviews. Hospitals have their own ranking levels, classified as Class A, B or C; with Class A having the highest rating. Patients like to go to hospitals with higher ratings, such as Class A. In China, doctors also have an offline title in their

hospital that represents their medical skills and experience, indicating whether they are a chief physician, associate chief physician, attending doctor etc., and these titles are reevaluated and issued by government agencies. Doctors’ online reputations are also evaluated by patients, reflecting not only the doctor’s medical level, but also the doctor’s popularity among patients. Patients prefer to make appointments with doctors who have prestigious titles, especially with the ‘chief’ title, and prefer to go highly ranked hospitals, regardless of the severity of their health problems. Given this situation, popular hospitals and doctors are constantly overloaded, thus not only decreasing efficiency and efficacy, but also increasing and exacerbating doctor-patient conflict. In contrast, less popular hospitals and doctors consistently have fewer patients to treat, resulting in considerable waste of medical resources. This calls for an examination of whether online health communities improve this situation in China and whether increasing online channels substitutes or complements offline channels for doctors of varied reputation levels. The next section examines the impact of online channels on offline channels in such online health communities. 3.2 The Impact of Online Channels on Offline Channels With the emergence of online communities, an increasing number of doctors adopt the interaction channels that the online health community provides. On one hand, online channels expand doctors’ reputations, which can help doctors attract more patients offline (complement). On the other hand, online channels can also provide other ways to treat illnesses or assuage anxiety, which can lead patients not to see doctors in hospitals (substitute). It is thus important to examine the complementary or substitution channel effects within the healthcare market. We next provide justification for two alternative perspectives related to the role that online channels play in affecting offline channels. From a complementary perspective, a number of studies suggest that the Internet has a distinct influence on offline sales [15, 50]. Many product or service receivers still rely on offline stores for the actual product or service purchase. Whereas the Internet gains increasing importance for information collection [50], online channels may have spillover effects, generating increased purchases in offline channels [15]. These studies emphasize the theoretical advantages of integrating online services with existing physical channels. For example, combinations of channels can be used to target different kinds of

service receivers and offer different kinds of services cost-effectively [51]. Due to the problem of time and distance, many patients may not quite understand the doctors’ medical ability. However, online health communities can help patients access information about doctors. Moreover, through written consultation and telephone interactions, patients can engage with doctors before going to the hospital. This online communication helps patients to get to know the doctor, thus reducing their uncertainty and sense of risk and enhancing their trust in the doctor and increasing OBSH utilizations. Based on these insights, we hypothesize that the more online services that patients use, the greater the use of OBSH. From a substitution perspective, authors suggest that there may be substitution by advertisers between print, television, and radio advertising channels [52-54]. Some product categories compete because they are able to serve a similar defining purpose and thus may have similar potential customers [55, 56]. Online written consultation, telephone consultation, and OBSH partly serve the same purposes, as in each mode the doctors can help patients to explain their check-up results, give a diagnosis, and make recommendations. Doctors can thus provide patients with alternative services for them to choose from, according to their situation. Without Internet medical, patients have to go to hospitals to see a doctor, whatever the health problem is. Now, patients can use online channels, such as online written consultation and online telephone consultation, to get advice from doctors and to avoid having appointments offline. In addition, some patients go to hospitals out of anxiety and uncertainty because they don’t know what’s wrong with them and hope to seek reassurance. Under this circumstance, by interacting with doctors through online channels could also solve their problems and reassure them. Online channels thus have the ability to solve some patients’ problems, as they can reduce the number of patients from taking hospital appointments. Based on these considerations, we can reasonably expect that the more patients that utilize online services, the lower the totals of OBSH. Based on our arguments above on complementary and substitution effects between online and offline channels, we need to ascertain the advantages of these effects in specific contexts. We propose both

complementary and substitution effects between online and offline channel, leading us to the following hypotheses: H1a: A doctor who provides patients with written consultation service has higher totals of Online Booking Service in Hospitals (OBSH) (complement). H1b: A doctor who provides patients with written consultation service has lower totals of OBSH (substitute). H2a: A doctor who provides patients with telephone consultation service has higher totals of OBSH (complement). H2b: A doctor who provides patients with telephone consultation service has lower totals of OBSH (substitute). 3.3 Reputation and Performance Reputation is defined as the extent to which buyers believe a company is honest and concerned about its customers [57]. Numerous empirical studies suggest reputation as one of the predominant factors in influencing sellers’ performance [16, 17, 58]. Online reputation can improve the interaction between buyers and sellers, decrease their risk, thereby increasing the trust and cooperation of both sides [59-61]. Sellers’ individual online reputation can also positively impact their sales [23, 62]. Doctors’ online reputation is the patients’ evaluation of doctors. Patients’ similar experiences can increase other patients’ trust in the doctor and decrease the risk [63, 64]. Doctors’ offline reputation is the title of the doctor evaluated by related government agencies. In China, doctors have their own offline title in hospital, which classifies them into several levels - chief physician, associate chief physician, attending doctor, etc., which reflects doctors’ professional ranking and medical capacities. In general, doctors’ online and offline reputations are both indices that reflect the doctor’s overall capability. We hypothesize that both online and offline reputations positively impact doctors’ performance, which is measured by the totals of doctor’s OBSH. H3a: A doctor’s online reputation positively impacts doctor’s OBSH totals. H3b: A doctor’s offline reputation positively impacts doctor’s OBSH totals.

3.4 The Moderating Effects of Doctor Reputation on Channel Effect Marketing literature describes reputation as a quality signal, which enables product or service receivers to build trust with providers, allowing consumers to exert both informational and normative influences on the product or service evaluations and purchase intentions of fellow consumers [65, 66]. According to Li et al. [67], product or service receivers’ trust in a provider can reduce perceived risk; therefore, they may be more confident in making transactions via new channels, because the existing channels carry over the good image that the providers have previously established. The relative salience of such favorable and unfavorable features when comparing online and offline shopping options undoubtedly varies across products, consumers, and situations [70]. The literature on which our study is based gives diverse views on these dynamics within the healthcare industry. Online channels can provide products that are identical with offline channels. Taking the example of a cup, a seller can sell it online or he can sell it in store, and no matter which channel the buyer chooses, he gets the same thing. This is not the case, however, for the healthcare industry, as doctors can provide similar services for patients online and offline, albeit the two services are partial substitutions. If patients choose to get advice online, they have to accept the risks associated with the fact that the doctor cannot communicate with them face-to-face, or look directly at the patient, listen to verbal cues, examine the patient physically, or even use the four diagnostic methods of Traditional Chinese Medicine (TCM). Regardless of how popular doctors are, they cannot provide the same service online. Patients tend to want to solve their health problems online because of time constraints, the limit of geographical positioning (time cost), or higher financial cost. They do not normally expect to receive the same level of service online as offline. Product or service receivers prefer stores that provide high quality products or services at a low price, but the clients also want to make the transaction quickly and pleasantly. Transaction-cost economics [69, 70] suggests that product or service buyers select providers in part to minimize transaction cost and uncertainty. In most circumstances, higher cost will decrease demand and increase switching [71]. Using offline channels will take more time and more money than using online channels to obtain a desired

object. According to law of demand, under the same situation, the demand of products will decrease when the price of products increases [72]. However, brand and quality moderate the relationship between price and purchasing intentions: good brand and high quality level can mitigate the impact of price on sale quantity [73]. Some research results show that reputation has a “premium effect” [25, 26, 74], which can moderate the relationship between price and purchase intentions [16, 25, 75]. Reputation may increase customers’ value perceptions, willingness to pay, and purchase intentions despite the price deviation [76-78]. The results of Cheema [26] indicate that price surcharges affect purchases more for sellers with lower customer ratings, a measure used as a proxy for seller reputation. Findings of Helm [79] confirm that the more favorable the perceived reputation, the less likely are customers to attribute negative motives for the price increase or price unfairness. Based on the above comments, when accessing a doctor with a higher reputation, patients are willing to pay more for the treatment; for example, they are more likely to spend more time and more money to go to hospital for face-to-face treatment. In this situation, patients will go to the hospital for the treatment directly rather than get information online. Based on this situation, the discrepancy between online channels and offline channels will be mitigated. H4a: A doctor’s higher online reputation mitigates the main effect between online written consultation and OBSH. H4b: A doctor’s higher offline reputation mitigates the main effect between online written consultation and OBSH. H4c: A doctor’s higher online reputation mitigates the main effect between telephone consultation and OBSH. H4d: A doctor’s higher offline reputation mitigates the main effect between telephone consultation and OBSH.

4. Methods

4.1 The Research Context Our research context is “Haodf online” (www.haodf.com), which is one of the most popular and professionally regarded online health communities in China. Several big companies have established cooperative relationships with “Haodf online,” such as Tencent, Sina, and Sohu. It was founded in 2006, and currently comprises more than 3,200 hospitals and more than 331,000 doctors. The platform automatically creates homepages for doctors based on a directory they have collected. On this platform, doctors can choose to offer online written consultation, telephone consultation, or OBSH, or all of the above. Patients can search for generalized health information, and/or ask doctors questions. Many unique attributes and services are available on “Haodf online” to help patients make better and more accurate selections that suit their needs. Patients in increasing numbers visit and use this website to get help from doctors online. Figure 2 is the overview of the “Haodf online” website. For English readers’ convenience, we also translated the original Chinese version of Haodf.com to English for all the figures about this website, including Figures 2-5. Via the “Haodf online” website, patients can choose from the channels (online written consultation, telephone consultation and OBSH) according to their needs. This platform has a formal and comprehensive reputation mechanism, which is important for this study.

Figure 2. “Haodf online” Website 4.2 Sample and Data Collection We developed a crawler to automatically download web pages of doctors and information about doctors from “Haodf online”. We collected a list of all doctors who provided OBSH in September 1, 2014. The whole process lasted a month, after which the information of 4,254 doctors was included in our research. For each doctor in our data set, we collected the corresponding service information, reputation information, and hospital information. In greater detail, the doctor service information included whether the doctors provided online written consultation service, whether they provided online telephone service, and the totals of OBSH they provided offline. The platform also reports how many virtual gifts patients gave to doctors, how many letters of thanks that patients wrote to doctors, and the number of doctor titles. From doctors’ homepages, patients found reputation information, accordingly: hospital information showed the hospital level, address, and telephone number. This information is summarized in Figures 3, 4 and 5.

Figure 3. Doctor Online and Offline Reputation

Figure 4. Three Service Channels

Figure 5. Hospital Information 4.3 Variables and Empirical Models Our empirical variables are shown in Table 1. Our study comprises two steps and has three dependent variables in all: 1) the totals of doctors’ OBSH, 2) whether the doctor provided online written consultation services, and 3) whether they provided telephone consultation services. Our major dependent variable OBSH_Totals is defined as the total number of a doctor’s OBSH, which refers to how many patients choose to use this online channel to make appointments with the doctor for an offline service. We used this variable because it is very important for the doctors and can help to measure their offline performance. In addition, we used two dummy variables, Written_Consultation_dummy and Telephone_Consultation_dummy, to measure whether each doctor provided online written consultation service and telephone consultation, respectively.

Table 1. Variables Description Variables Dependent variables

Independent and control variables

First step: Probit Models Variable symbol Explanation Written_Consultation_dummy Whether online written consultation is provided. Telephone_Consultation_dummy Whether telephone consultation is provided. DTitle_dummy1, Doctor has own offline title, DTitle_dummy2 which can represent their ability offline. Doctors have several titles, chief physician, associate chief physician, attending doctor and others. The number of other title doctors is small, so we use two dummy variables to measure doctor titles Hlevel_dummy Hospital has their own levels, which can represent their ability, including level A, B and C. Level A is better than B, and level B is better than C. The number of level A hospital is small, so we use one dummy variable.

Second step OLS Model Variable symbol LnOBSH_Totals

Explanation The number of doctors’ offline appointments

LnGift

The number of virtual gifts a doctor receives

Dtitle_dummy1, Dtitle_dummy2

Omit

Imr_Witten_Consultation

Inverse mills ratio of Probit model Inverse mills ratio of Probit model

Imr_Telehone_Consultation

The independent variables are whether a doctor provided online written consultation services, or whether they provided telephone consultations. In addition, doctors’ online reputation and offline reputation were also measured. We used the number of virtual gifts that patients gave in order to measure doctors’ online reputation, and used doctors’ title to measure their offline reputation. In “Haodf online”, when patients perceive that the doctor offers really good service, they can buy virtual gifts from the website and send them to the doctor. Since patients need to pay real money for virtual gifts, the number of virtual gifts can accurately represent doctors’ reputations, because the virtual gifts represent their appreciation for a doctor, indicating that they are willing to spend extra money on this doctor. For doctors’ offline reputation, doctors have several titles: chief physician, associate chief physician, attending doctor, and other titles. Since the number of other titles was very small, we combined it with the attending doctor title and used two dummy variables: DTitle_dummy1 and DTitle_dummy2 to measure the doctor’s offline reputation. For the control variables, we collected information from the hospital to which each doctor belonged, especially the level of the hospital. Hospital level represented hospitals’ ability: level A, B or C, with A being the best. Although there are three levels of Chinese hospitals, our sample data included mostly level A and B, and thus we combined B with C and used one dummy variable: Hlevel_dummy to measure hospital level. Dummy variables in our model are explained as follows:

=1, when doctor provide online consutaltion service

Written _ Consultation _ dummy 

=0,

others

=1, when doctor provide telephone consutaltion service

Telephone _ Consultation _ dummy 

=0,

others

=1, when doctor title is chief physician

DTitle _ dummy1 

=0,

others

=1, when doctor title is associate chief physician

DTitle _ dummy 2 

=0,

others

=1, when hospital level is A

HLevel _ dummy 

=0,

others

One potential threat to our identification of the channel effect is that the doctors’ channel availability may be endogenous. Some factors may affect whether doctors open the online written consultation or telephone consultation, which leads to a potential self-selection bias, such that those doctors’ performance difference may come from the factors that affect doctors’ availability of certain channels rather than the availability of the channels itself. To control for this, we adopt the Heckman Selection model [80] by estimating the probability that a certain doctor chooses to open the online channel, and add this probability as a control variable in our main estimation. This can help to control for the heterogeneities between doctors who open the online channel and doctors who do not. Thus, our estimation is conducted in two steps. In the first step, we used the Probit model [81] to check which factors impact doctors’ decisions about whether or not to provide the two online services. Because the dependent variable of one Probit model acts as the independent variable in the other Probit model, we used a Seemingly Unrelated Regression (SUR) model [82] to eliminate the impact of potential correlated error term in the two Probit models. In the second step, we used the predicted value from the Probit model to calculate the invert mills ratio for each physician and include it in the regression models to control for the self-selection bias, and then took the logarithmic value of the number of OBSH and virtual gifts to stabilize the variance. Our empirical models are shown as follows: Probit Models

Written _ Consultation _ dummy  11Telephone _ Consultation _ dummy  12 Hlevel _ dummy  13 DTitle _ dummy1  14 DTitle _ dummy 2+ 1 Telephone _ Consultation _ dummy   21Written _ Consultation _ dummy   22 Hlevel _ dummy   23 DTitle _ dummy1   24 DTitle _ dummy 2 + 2 OLS Model

LnOBSH _ Totals   31Written _ Consultation _ dummy   32Telephone _ Consultation _ dummy   33 LnGift   34 DTitle _ dummy1   35 DTitle _ dummy 2   36Written _ Consultation _ dummy  LnGift   37Written _ Consultation _ dummy  DTitle _ dummy1   38Written _ Consultation _ dummy  DTitle _ dummy 2   39Telephone _ Consultation _ dummy  LnGift   310Telephone _ Consultation _ dummy  DTitle _ dummy1   311Telephone _ Consultation _ dummy  DTitle _ dummy 2 + 312 Imr_Written _ Consultation   313 Imr _ Telephone _ Consultation+ 3

5. Results 5.1 Descriptive Statistic and Correlations We estimated our models using Probit, SUR, and OLS methods, and all our empirical models were done by STATA. We used SUR to check for bias in our models, and our results show there is practically no bias error in our two Probit models. We then used the OLS model to estimate model parameters and test our hypotheses. We discuss our results in the following sections. The descriptive statistics and correlations for the key variables used in the analysis are presented in Table 2. The key observations from the correlations of the variables reveal that the variables are correlated with the totals of offline OBSH, and the correlation between written consultation and telephone consultation is low. The correlations between independent variables and control variables are low, which has helped to get stable results. Table 2. Description and Correlation Mean

Standard 1 Deviation 1.686 1

1.LnOBSH_Totals 3.182 2.Witten_Consultation_ 0.910 0.282 dummy 3.Telephone_Consultati 0.460 0.498 on_dummy 4.LnGift 2.342 1.836 5.DTitle_dummy1 0.530 0.499 6.DTitle_dummy2 0.390 0.489 7.Hlevel_dummy 0.980 0.135 Notes: * p < 0.05, ** p < 0.01, *** p < 0.001.

2

3

4

5

6

1 0.109** -0.066** -0.057**

1 -0.853** 1 -0.036* 0.026

7

-0.089** 1 0.164**

0.077**

1

0.539** 0.220** -0.168** 0.013

0.204** -0.055** 0.026 -0.030*

0.300** -0.050** 0.058** -0.020

1

5.2 Empirical Results We applied the Probit, SUR and OLS models to examine the channel effect, and the empirical results for this are shown in Table 3. From the Probit results, we find there is a positive impact of whether provide online written consultation services on whether provide telephone consultation (p<0.001), and there is also a positive impact on whether provide telephone consultation on whether provide online written consultation (p<0.001). Some doctors use both online services and some use neither. Table 3. Empirical Model Results

Variables

Hlevel_dummy Witten_Consultation_dummy Telephone_Consultation_dumm y LnGift DTitle_dummy1 DTitle_dummy2 Witten_Consultation_dummy*L nGift Witten_Consultation_dummy*D Title_dummy1 Witten_Consultation_dummy*D Title_dummy2 Telephone_Consultation_dumm y*LnGift Telephone_Consultation_dumm y*DTitle_dummy1 Telephone_Consultation_dumm y*DTitle_dummy2

First-step Telephone_Consultation_d Witten_Consultation_dummy ummy Model1(Pr Model1(Prob Model2(SU Model2(SUR) obit) it) R) -0.624* -0.624* -0.176 -0.176 0.347*** 0.347*** 2.349*** 2.349***

-0.572*** -0.437**

-0.572*** -0.437**

0.011 0.155*

0.011 0.155*

Second-step (OLS) LnOBSH_Tot als 3.320+ -9.854*** 0.433*** 2.318*** 2.123*** 0.053 -1.586** -1.417** 0.064** -0.256 -0.131

Imr_Witten_Consultation -0.890 Imr_Telehone_Consultation 6.169*** LR chi2(4) 53.73 40.81 Prob>chi2 0.000 0.000 Pseudo R2 0.021 0.007 Log likelihood -1230.139 -2914.483 F(13, 4240) 182.41 Prob>F 0.000 Adj-R2 0.358 Root MSE 1.352 Notes: ***p<0.001, **p<0.01, *p<0.05, +p<0.10. N=4254. OLS present Ordinary Least Squares.

From the adjusted R-square and F value of OLS, our independent variables explain dependent

variables well. Hypotheses H1a, H1b, H2a and H2b test the complementary versus substitution relationship between doctor online service channels and offline service channels. Regarding the doctor online written consultations, there is a significant complementary relationship between online written consultation and the totals of OBSH offline (p<0.10), and thus hypothesis H1a is weakly supported. Regarding the doctor telephone consultations, there is a significant substitution relationship between telephone consultations and the totals of OBSH (p<0.001), supporting hypothesis H2b. Hypotheses H3a and H3b test the positive relationship between doctor reputation and the totals of doctor OBSH offline. In terms of online reputation, doctors with significantly more virtual gifts have higher totals of OBSH than do those with fewer virtual gifts (p<0.001), and thus hypothesis H3a is supported. In terms of doctors’ offline reputations, doctors with a chief physician title attract more patients to visit than do attending doctors or those with other titles (p<0.001), and doctors with an associate chief physician title will also attract more patients to visit than do doctors with attending doctor titles or with other titles (p<0.001).Thus, hypothesis H3b is supported. Hypotheses H4a-H4d test the moderating effect of doctor reputation on the relationship between online service and offline service. We found that the interaction effects between offline reputation and online written consultation on totals of OBSH are supported (p<0.01), which then means hypothesis H4b is supported, but there is no moderating effect of doctor’s online reputation on the relationship between online written consultation and OBSH. We also found interaction effects between online reputation and telephone consultation on totals of OBSH are supported (p<0.01), and hypothesis H4c is supported. However, there is also no moderating effect of doctor’s offline reputation on the relationship between telephone consultation and OBSH. For doctors who received more virtual gifts, the impact of telephone consultation on OBSH is smaller than it is for doctors who received fewer virtual gifts. For doctors with higher title, the impact of online written consultation on OBSH is smaller than it is for doctors with lower title. 5.3 Robustness check We added severity of disease as a control variable in our model. Like other online health community

websites, Haodf website divides physicians based on their specialties. National Health Statistics Yearbook in 2013 [86] lists the mortality rate of various diseases. We classified specialties based on diseases and use ranks in table 5 to measure severity of disease according to mortality rate of various diseases. Table 5 shows the mortality rate of main diseases, and table 6 and 7 show the description and correlation results and empirical results. Our results show that the impact of severity of diseases is positive. Doctors who usually treat high-mortality diseases have higher OBSH. However, there is no significant interaction effect between channel effects and severity of disease. From Table 6, we can see that the severity of disease is related with doctor’s title and hospital level, implying doctors with higher titles and doctors who work in higher-level hospitals treat more serious diseases. Our results show the channel effects exist even we control for the impact of severity of disease. Table 5. Mortality Rate of Main Diseases Disease Name

Mortality Rate (1/100,000)

Rank

Malignant tumor related diseases

164.51

1

Heart Diseases

131.64

2

Cerebrovascular related diseases

120.33

3

Respiratory related diseases

75.59

4

Injure and Intoxication

34.79

5

Other diseases

23.82

6

Endocrine, metabolic and nutritional related diseases

17.32

7

Digestive system diseases

15.25

8

Nervous system diseases

6.86

9

Urogenital diseases

6.30

10

…

…

…

Table 6. Description and Correlation Mean

Standard Deviation

1

2

3

4

5

1.LnOBSH_Totals

3.182

1.686

1

2.Witten_Consultation_dummy

0.910

0.282

-0.089**

1

3.Telephone_Consultation_dummy

0.460

0.498

0.164**

0.077**

1

4.LnGift

2.342

1.836

0.539**

0.204**

0.300**

1

5.DTitle_dummy1

0.530

0.499

0.220**

-0.055**

-0.050**

0.109**

1

6.DTitle_dummy2

0.390

0.489

-0.168**

0.026

0.058**

-0.066**

-0.853**

6

1

7

7.Hlevel_dummy 8.Severity of diseases

0.980 5.560

0.135 1.325

-0.030*

0.013 0.021

*

-0.022

-0.020 0.041

*

-0.057**

-0.036*

0.026

1

0.010

0.323**

0.225**

0.226**

Notes: * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 7. Empirical Model Results

Variables

Hlevel_dummy Witten_Consultation_dummy Telephone_Consultation_dummy LnGift DTitle_dummy1 DTitle_dummy2 Witten_Consultation_dummy*LnGi ft Witten_Consultation_dummy*DTitl e_dummy1 Witten_Consultation_dummy*DTitl e_dummy2 Telephone_Consultation_dummy*L nGift Telephone_Consultation_dummy* DTitle_dummy1 Telephone_Consultation_dummy* DTitle_dummy2 Severity of diseases Witten_Consultation_dummy* Severity of diseases Telephone_Consultation_dummy* Severity of diseases Imr_Witten_Consultation Imr_Telehone_Consultation Prob>chi2 Pseudo R2 Prob>F Adj-R2

First-step Telephone_Consultation_d Witten_Consultation_dummy ummy Model1(Probi Model1(Pr Model2(SUR) Model2(SUR) t) obit) -0.524* -0.524* -0.171 -0.171 0.407*** 0.407*** 2.110*** 2.110*** -0.512*** -0.430**

-0.512*** -0.430**

0.010 0.160*

0.010 0.160*

Second-step (OLS) LnOBSH_Tot als 3.310+ -9.980*** 0.370** 2.312** 2.133** 0.052 -1.600** -1.432** 0.053** -0.322 -0.135

-0.125

-0.125

0.230*

0.230*

0.031* 0.010 0.022

-0.102 6.233** 0.000 0.021

0.000 0.007 0.000 0.368

6. Discussion 6.1 Result Analysis Our empirical results show that most of the hypotheses are supported, and we highlight the findings from the Probit model, e.g., whether provision of consultation services has a positive impact on whether

to provide telephone services, and vice versa. We find doctors like to use both written consultation services and telephone consultation services (or they may choose to use neither). One possible explanation could be that for appointments, doctors have to help patients during scheduled appointments. However, for written consultation and telephone services, no matter when or where, doctors can choose their own times to do so, which is very convenient for doctors. Since both services are rendered online, doctors are likely to provide both of these services (or neither). For the relationship between different channels, the results are interesting. While online written consultation complements OBSH, telephone consultation substitutes for it. One possible explanation could be that, in the “Haodf online” website, online written consultation is free for the first three times and then patients need to pay for further questions; according to our statistics (from the website data), most patients (over 90%) using the online health platform ask fewer than three questions. By first having a written consultation with a doctor, patients gain a basic understanding of their disease and then they can see the doctor in the hospital for further details. The telephone consultation fee is much higher than that of the online written consultation channel. Most patients would like to choose only one. Given the limitation of time and distance, many patients are not able to see a doctor in a hospital. As the telephone channel is more convenient and takes less time, patients prefer to call doctors instead of going to hospitals. Based on this, the relationship between telephone consultation and OBSH is a substitution one. Another possible explanation lays in the different levels of information richness across different channels that physicians and patients interact with. Among the three channels, OBSH service makes patients get the most information because patients and physicians communicate with each other face-to-face. On the other hand, based on Information Richness Theory [83, 84], telephone consultation service offers medium level of information richness, as it provides non-stop communications for patients and physicians in a certain period of time. Online written consultation service provides the lowest level of information richness, because patients and physicians need to communicate in the form of discontinued texts. As OBSH offers the highest level of information richness, patients who do not want to go to the physical hospital and use a substitute channel would prefer a channel with higher level of information

richness, at least not too far away from the OBSH channel. Thus, the telephone consultation channel may behave more like a substitute service for OBSH service, while the online written consultation channel may behave more like a complement service for OBSH service. We find that doctors’ reputation positively impacts on OBSH totals, a result which is in line with existing literature [16-19]. Moreover, our research proves that both online and offline reputation have a positive impact on doctors’ performance (see Table 3), suggesting that doctors can work hard both in online communities and offline hospitals to improve their reputation. Doctors who are in small or low level hospitals can improve their performance by participating in online communities, which can attract patients in the same way that an offline title does. This indicates that an online health community can help doctors with low titles to realize their life aspirations. For interaction effects, our results show that a doctor’s reputation mitigates the relationship between online and offline service (see Table 3). A doctor’s title mitigates the relationship between online written consultation and OBSH, but it does not have any significant impact on the relationship between online telephone consultation and OBSH. Doctors’ online reputation mitigates the relationship between online telephone consultation and OBSH, but it does not have significant impact on the relationship between online written consultation and OBSH. For further explanation of these results, see Table 4, and the interaction effects in Figure 6. For the moderate effect of doctor’s online reputation on the relationship between telephone online and OBSH, we use the mean virtual gifts amount plus one standard deviation to present high reputation (high virtual gift), and the mean virtual gifts amount minus one standard deviation to present low reputation (low virtual gift), and we found both value of low reputation and high reputation located within the scope of gift. See the following formula:

High gift=mean value (LnGift ) +standard deviation (LnGift ) Low gift=mean value (LnGift ) - standard deviation (LnGift ) In the online written consultation service of the “Haodf online” website, patients can upload their medical data to doctors, which can help doctors gain a clearer understanding for the diagnoses. This is in

stark contrast to telephone consultation, where patients can only communicate by telephone and doctors are notable to see data about the patient. Table 4. The Moderating Effect of Reputation on Channel Effect

Interaction Item Written consultation→online booking service in hospital (OBSH) totals Telephone consultation→OBSH totals

Moderating Effect Online reputation Doctor title Medical Medical skill Popularity Total effect skill Negative Negative Positive No significant effect effect effect effect No significant Positive No effect Positive effect effect effect

Figure 6. Interaction Effect between Doctor Reputation and Online Channel on “Online Booking Service in Hospital” Totals

Doctors with higher titles usually have greater medical skills. Because of lower time pressure in online written consultation, they have more time to analyze a patient’s condition and disease. They can use the support materials from patients, such as the “check-up result” function. The difference of doctor’s medical skill level is more likely to be shown in the online health community. It is also true that in China,

doctors with high titles are very often fully booked, and it is extremely difficult for patients to get an appointment with these doctors. Based on the above arguments, doctors’ titles can mitigate the relationship between online written consultation and OBSH. However, for telephone consultation, because of short time pressure and no supporting material, a doctor’s medical skill is hard to ascertain, and thus we found no evidence on the moderating effect of doctor title on the relationship between telephone online and OBSH. We further discuss doctors’ higher online reputations with respect to higher medical skills and popularity: 1) Medical skills. We believe the medical skill function of online reputation has a similar impact to doctor’s title. The relationship between written consultation and OBSH shows that it is helpful for doctors to solve problems online, which mitigates the channel effect. The relationship between telephone consultation and OBSH does not have any significant impact. 2) Popularity. Regarding the relationship between written consultation and OBSH, higher popularity facilitates the transition of patients from online to offline [85], and so there is a positive moderating effect. When we consider medical skill and popularity together, we conclude that for the relationship between written consultation and OBSH, the impact of medical skill is negative and the impact of popularity is positive, and thus the final impact depends on the extent of these two impacts. Our research shows that these two effects are similar, and there is no significant impact of online reputation on channel effect. For the relationship between telephone consultation and OBSH, higher popularity facilitates the transition of patients from online to offline, suggesting a positive moderating effect. When we consider both medical skill and popularity together, we find that the final impact of online popularity on channel effect is positive. 7. Implications and limitations 7.1 Implications This paper studies the impact of online channels on offline channels, and how the channel effect changes in correlation with the reputation level. Our study contributes to knowledge in several key ways.

First, despite some studies indicating that there are channel effects in the marketplace [6, 15], literature rarely uses empirical methods to validate claims. Our study is among the first to use real data to empirically examine channel effect, especially in healthcare, which is a universally beneficial sector. The research context gives us the opportunity to study the effects of two different online channels on offline channels, and our results show that there are different channel effects in healthcare. Second, our study contributes to existing theory of channel effects by complementing traditional market and e-commerce studies, particularly as the products (service) of online and offline in our study are different. Related literature on channel effect is generally based on the same product or service [46, 48, 49]. Our results show that when there are differences between online and offline products or service, the channel effects are also different. Third, our study contributes to existing theory of channel effects and reputation by hypothesizing and empirically testing the moderating influence of reputation on the relationship between online channels and offline channels. By analyzing existing literature, we found that there are few studies combining them; they are mostly researched separately. Although some researchers have studied the importance of reputation [17, 23], few studies consider both online and offline reputation, and their effect on the relationship between online and offline channels. Our study enriches existing literature by indicating that the channel effects vary for different doctors. The paper also makes contributions to practice. First, multi-channel utilization is on the rise, with practitioners seeking guidance on how to balance different channels. We believe our analysis provides insights that are helpful to doctors as they consider implementing a multi-channel strategy. As we saw in our reputation-channel effect analysis, channel effects can vary substantially from doctor to doctor because of the differences in their reputations. Our study shows that doctors can choose to provide services according to their reputation condition to maximize their benefits. If doctors with a higher title have extra time, they can provide more consultation services because patients would generally find it hard to make an appointment with them. Second, our study highlights the importance of rethinking the nature of reputation in relation to

multichannel strategies. Our study shows that both online and offline reputation has a positive impact on doctor’s performance. This result indicates that even if doctors have a lower title offline, they can improve their career outcomes by working hard in an online health community. Our study has proved that online health communities benefit not only patients, but also doctors. This result can encourage doctors to attract more patients and achieve their career goals by participating in online communities. Thirdly, our study gives suggestions for the online health communities’ managers. Our results show there are both complementary and substitution effect between online and offline channels. For written consultation service, which complements OBSH service, websites’ managers can encourage physicians to provide this service, especially for these physicians who do not have many patients daily. For telephone consultation service, which substitutes OBSH service, although this service has more information asymmetry compared with face-to-face service, it can help patients resolve troubles, such as geographic restrictions and time limitation, especially for these illnesses that do not need to go to the hospitals. Websites’ managers can encourage physicians to provide this service, especially for these physicians who have a lot of patients, such as “famous physicians”. 7.2 Limitations This paper has several limitations and future directions. First, we used cross-sectional analysis, and our future research can adopt longitudinal data to research dynamic changes over time. Second, we studied one context. This helps us improve the internal validity, but it may also reduce the generalizability of our findings, and our future research should validate our results in other service contexts. Third, we did not analyze the relationship between online written consultation and telephone consultation, as there is a big difference between consultation charges and the higher telephone charges to patients, and our future research will study the interaction between the two services in health communities. Fourth, multiple methods should be used to understand the true significance of these interesting results, such as quality study. Fifth, cost of different services should be considered in future study to analyze trend significance by varying prices. We believe our current findings demonstrate a strong relationship between online health communities and offline hospital communication with an important moderating role for reputation.

LNJ contributed to the conceptualization and design of the study, and the revisions of the manuscript. WH contributed to the conceptualization and design of the study, the collection and analysis of the required information. Both authors read and approved the final manuscript.

The authors declare that they have no competing interests.

What was already known. 1. Some studies indicating that there are channel effects in the marketplace, literature rarely uses empirical methods to validate claims. 2. Related literature on channel effect is generally based on the same product or service. 3. Although some researchers have studied the importance of reputation, few studies consider both online and offline reputation, and their effect on the relationship between online and offline channels.

What this study has added. 1. Our study is among the first to use real data to empirically examine channel effect, especially in healthcare, which is a universally beneficial sector. 2. Our results show that when there are differences between online and offline products or service, the channel effects are also different. 3. Our study enriches existing literature by hypothesizing and empirically testing the moderating influence of reputation on the relationship between online channels and offline

channels.

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