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Many markets have traditionally been dominated by a few best-selling products, and this is also the case for the health care industry. However, we do not know whether the market will be more or less concentrated when health care services are delivered online (known as E-consultation), nor do we know how to reduce the concentration of the E-consultation market.
The aim of this study was to investigate the concentration of the E-consultation market and how to reduce its concentration through information disclosure mechanisms (online reputation and self-representation).
We employed a secondary data econometric analysis using transaction data obtained from an E-consultation Website (haodf.com) for three diseases (infantile pneumonia, diabetes, and pancreatic cancer) from 2008 to 2015. We included 2439 doctors in the analysis.
The E-consultation market largely follows the 20/80 principle, namely that approximately 80% of orders are fulfilled by nearly 20% of doctors. This is much higher than the offline health care market. Meanwhile, the market served by doctors with strong online reputations (beta=0.207,
When health care services are delivered online, the market will be more concentrated (known as the “Superstar” effect), indicating poor service efficiency for society as a whole. To reduce market concentration, E-consultation websites should provide important design elements such as ratings of doctors (user feedback), articles contributed by doctors, and free consultation services (online representation). A possible and important way to reduce the market concentration of the E-consultation market is to accumulate enough highly rated or highly self-represented doctors.
The Pareto principle (also known as the 80/20 rule) states that, in many cases, approximately 80% of the effects result from 20% of the causes. The Pareto principle is very popular in the economic market, as it indicates that a small proportion (eg, 20%) of products in a market often generate a large proportion (eg, 80%) of sales [
Not surprisingly, the Pareto principle also applies to the health care service market. In the health care service market, a few of the best hospitals or doctors have a much higher market share than do ordinary hospitals or doctors [
A recent trend in eHealth is delivering health care services online [
The new technologies embedded in E-consultation are not limited to digital communications, computing, and storage but also involve a qualitative transformation in search tools, recommendation tools, and social network technologies [
In this study, we aim to investigate the following research questions:
RQ1: Will E-consultation be more of a long-tail or a superstar market? Or, will the E-consultation market be less concentrated or more concentrated than the offline market?
RQ2: Can information disclosure mechanisms (the doctor’s online reputation and self-representation) help reduce market concentration?
Choosing a doctor on a website is totally different from choosing a doctor at an offline hospital. A significant difference is the information available to the user when making a decision. With the help of information technology such as search engines, recommendation tools, and social networking technologies, the user can easily reach more doctors (especially unknown doctors) at a much lower cost than before. In the traditional offline context, the user’s choice set of doctors is quite small. The user usually chooses a doctor near their home or workplace. However, in the online context, the user can choose any doctor nationwide with just a few clicks of the mouse. This means that the choice set in the context of E-consultation is much larger, and users have more of an opportunity to choose unknown doctors than ever before. Thus, the online market will be less concentrated on a small number of high-profile doctors, creating a long-tail effect.
Another possible consequence of E-consultation is the superstar effect, also known as the Matthew Effect or “the rich get richer.” This is because popular doctors enjoy greater visibility on E-consultation platforms (eg, they are ranked highly by search engines or recommended preferentially by websites). As a consequence, the very good and popular doctors have a greater chance than before of being identified at the national level, which further increases their chance of being chosen by users. Thus, the online market will be more concentrated on a small number of famous doctors, creating a superstar effect.
In summary, both the long-tail and superstar effects may exist in the E-consultation context. We cannot know which effect will be dominant without an empirical study. Therefore, we propose the following two competitive hypotheses:
H1a: The online market is less concentrated than the offline market.
H1b: The online market is more concentrated than the offline market.
Health care is a market with high information asymmetry. Information asymmetry models assume that at least one party to a transaction has relevant information while the others do not. In the case of E-consultation, doctors have more information about their own service quality than do the patients. Although doctors know their own service quality, patients have little information on this very important question. This situation of information asymmetry creates an imbalance of power in transactions, which can sometimes cause the transactions to go awry—a type of market failure in a worst-case scenario.
According to signaling theory [
In the E-consultation context, doctors send information about their service quality to patients. After receiving this information, patients may change their judgment about doctors’ service quality and further change their choice of doctors. In this study, we focus on two signals that a doctor can send about their service quality on an E-consultation website, specifically, online reputation and online self-representation.
An online reputation (also known as online word-of-mouth) is built based on feedback from patients. E-consultation websites usually provide a feature known as “rate a doctor.” A patient who has visited the doctor previously can write a review of the doctor in terms of technical competence, interpersonal manner, systems issues, etc. The online reputation system is very popular on E-commerce platforms and has been demonstrated as a reliable mechanism to reduce market information asymmetry. For example, eBay uses a system of customer feedback to publicly rate each member. Amazon [
If the E-consultation website does not provide an online reputation feature, the user judges the doctor’s service quality based only on the doctor’s professional standing (eg, director, associate director). Therefore, the user’s consideration set is small because only those doctors with high offline positions will be considered. When the E-consultation website does provide an online reputation feature, users have more clues to evaluate the doctor’s service quality. If the market has many doctors with strong reputations, users will consider those who are highly rated but perhaps have lower offline positions. This means that the consideration set is enlarged. However, if the market is full of doctors with poor reputations, users will not include those poorly rated doctors in the consideration set. This means the consideration set remains at the same size or is even smaller (if doctors with high positions are poorly rated). Thus, having a market with highly rated doctors is very important. If the market has many highly rated doctors, market efficiency will be improved because users have more credible doctors from which to choose (ie, the supply of high-quality doctors is increased). In the same vein, market efficiency will not be improved if the market has few highly rated doctors. Therefore, we propose the following hypothesis:
H2: A market served by many doctors with strong online reputations is less concentrated than a market served by many doctors with poor online reputations.
Self-representation is the activity a doctor commits online for the purpose of sending quality information. There are several ways for doctors to represent themselves on an E-consultation website. For example, a doctor can post articles or provide free consultation services. Such efforts are another type of signal the doctor sends to users. The user can evaluate the doctor’s service quality in terms of the efforts reflected online. For example, doctors who post popular medical science articles demonstrate not only their medical knowledge and skills but also their positive attitudes toward E-consultation as well as their Internet savvy. Meanwhile, the quality and number of free consultation services provided are good indicators of the doctor’s expertise and social responsibility.
Therefore, when an E-consultation website provides self-representation features, the users have more information with which to judge the doctor’s service quality. If the market has many doctors representing and promoting themselves, users will consider these highly represented doctors, who may not be well known offline. This means the consideration set, as well as the supply of high quality doctors, is enlarged. However, if the market is full of doctors with low self-representation, users will not consider these low-effort doctors, and thus the consideration set remains the same. For the same reason, if the market has many highly represented doctors, market efficiency will be improved because users will have more credible doctors from which to choose. Therefore, we propose the following hypothesis:
H3: A market served by many doctors who are highly represented online is less concentrated than a market served by many doctors who are not well represented online.
In summary, we aim to investigate the concentration of the E-consultation market and how to reduce its concentration through information disclosure mechanisms. We hypothesize that the online market is less or more concentrated than the offline market, and the online reputation or self-representation can be used to reduce market concentration.
We employ a secondary data analysis as the research method. Secondary data refers to data that were collected by someone other than the researcher. Primary data, by contrast, are collected by the investigator conducting the research. In this study, the data were originally collected by the E-consultation website, haodf.com [
We collected data from Good Doctor (haodf.com [
Following Brynjolfsson et al’s work [
Online reputation is measured by the number of votes, letters of thanks, and gifts received by the doctor (the three variables are standardized and then averaged to create a composite variable). The review score is not used to measure online reputation in this study because we observe a ceiling effect (most doctors have a top score, making it very difficult to distinguish doctors). Self-representation is measured by the number of scientific papers the doctor has contributed and the number of free services they have provided (the two variables are standardized and then averaged to create a composite variable).
Control variables include the doctor’s position, hospital level, service price, and duration of providing online service. Position is measured on a scale of 1-5, with 1 being the lowest and 5 the highest. Hospital level is measured on a scale of 1-3, with 1 being the lowest and 3 the highest. Service price is measured by the service fee (in Chinese Yuan) per phone call. Duration is measured by the number of months since the doctor’s homepage was established.
The descriptive statistics of variables used in this study are shown in
Descriptive statistics.
Variable | Observations | Mean | SD | Min. | Max. |
Order number | 2439 | 341.360 | 826.100 | 1 | 12518 |
Order rank | 2439 | 461.880 | 322.077 | 1 | 1231 |
Vote | 2439 | 21.320 | 34.134 | 1 | 429 |
Gift | 2439 | 17.791 | 61.205 | 0 | 1003 |
Thank-you letter | 2439 | 5.988 | 13.203 | 0 | 157 |
Reputation | 2439 | 0.000 | 0.922 | -0.447 | 13.162 |
Articles | 2439 | 13.242 | 145.257 | 0 | 6871 |
Free service | 2439 | 319.798 | 783.135 | 0 | 10876 |
Self-represent | 2439 | 0.000 | 0.733 | -0.250 | 23.536 |
Position | 2439 | 4.221 | 1.067 | 1 | 5 |
Hospital level | 2439 | 2.824 | 0.529 | 1 | 3 |
Service price | 2439 | 147.984 | 44.283 | 0 | 1200 |
Online duration | 2439 | 53.024 | 27.465 | 1 | 94 |
Variable correlations (Pearson correlation coefficient).
Order number | Reputation | Self-endeavor | Position | Hospital level | Service price | Online duration | |
Order number | 1 | ||||||
Reputation | .581 | 1 | |||||
Self-represent | .715 | .428 | 1 | ||||
Position | .001 | .090 | .018 | 1 | |||
Hospital level | .018 | .004 | .019 | .062 | 1 | ||
Service price | .040 | .161 | .012 | .118 | .009 | 1 | |
Online duration | .200 | .290 | .183 | .106 | -.049 | .079 | 1 |
Regression results.
Variable | Model 1 (standard error) | Model 2 (standard error) | Model 3 (standard error) | Model 4 (standard error) |
Position | -0.082a (0.043) | -0.007 (0.021) | -0.001 (0.019) | 0.005 (0.018) |
Level | 0.119 (0.086) | 0.007 (0.041) | 0.010 (0.038) | 0.014 (0.035) |
Price | 0.001 (0.001) | 0.0003 (0.0005) | 0.0004 (0.0005) | -0.0003 (0.0004) |
Duration | -0.022 (0.002) | -0.001 (0.010) | -0.0002 (0.001) | 0.001 (0.001) |
Lrank | -1.950 (0.022) | -2.086 (0.024) | -2.301 (0.024) | |
Reputation | -1.011 (0.052) | |||
Reputation*Lrank | 0.207 (0.012) | |||
Self-represent | -2.024 (0.066) | |||
Self-represent*Lrank | 0.386 (0.014) | |||
Constant | 2.439 (0.330) | 15.056 (0.211) | 15.840 (0.204) | 17.266 (0.200) |
R2 | 0.068 | 0.786 | 0.815 | 0.845 |
a
The regression results are shown in
Model 2 includes the order rank. The results from Model 2 show that the order rank is negatively related to the number of orders (beta1 =-1.950,
Model 3 focuses on the interaction between reputation and order rank. The results of Model 3 reveal a significant negative interaction between online reputation and rank (beta4 =0.207,
Model 4 focuses on the interaction between self-representation and order rank. The results of Model 4 reveal a significant negative interaction between online self-representation and rank (beta5 =0.386,
We ran a robustness check by using alternative measures for reputation and self-endeavor and got similar results (see
Lorenz curve.
The interaction between online reputation and order rank.
The interaction between online self-representation and order rank.
In this study, we found the E-consultation market to be more concentrated than the offline health care market, and both online reputation and self-representation help reduce market concentration. Specifically, we found the following. First, the E-consultation market is more concentrated than the offline health care market. In other words, the E-consultation market is more of a superstar market than a long-tail one.
Second, the market served by many doctors with strong online reputations is less concentrated than the market served by many doctors with poor online reputations.
Third, the market served by many doctors with high levels of online self-representation is less concentrated than the market served by many doctors with low levels of online self-representation.
Many prior studies have investigated the effect of the Internet on sales concentration. One of the most frequently cited phenomena is the long-tail effect (ie, the online market is less concentrated than the offline one). The main drivers of the long-tail effect come from both the supply side and the demand side [
Another frequently cited phenomenon is the superstar effect (ie, the online market is more concentrated than the offline one). The superstar phenomenon emerges when a comparatively small number of participants excel, surpass others in their field, and reap much greater rewards [
Previous studies of the impact of the Internet on market concentration mainly focus on the business context. We do not know of any studies investigating the impact of the Internet on health care market concentration. The results of this study show that the E-consultation market will be more of a superstar market than a long-tail market, revealing a “rich-getting-richer” picture. Some actions (eg, providing user feedback, allowing doctors self-representation, the adoption of human or automated medical guidance) must be taken to reduce this undesirable outcome.
There are previous studies on the concentration of the health care market. The most frequently investigated topic is the impact of market concentration on service prices. Previous studies reveal that higher market concentration usually leads to higher service prices [
Existing studies on health care market concentration are mainly conducted at the hospital level. The major reason is that most data are available at the hospital level.
E-consultation websites and historical transaction data provide a good opportunity to study market concentration at the level of individual doctors. Therefore, an important contribution of this study compared to previous studies is the unit of analysis. In addition, most previous studies are interested in the consequences of market concentration. However, we are interested in how to build a more or less concentrated health care market.
Our research offers several important theoretical contributions. First, this study investigates, for the first time, the important question of market concentration in the E-consultation context and compares it with the traditional offline health care market. The results indicate a superstar market rather than a long-tail market.
Second, previous studies on health care market concentration have mainly been conducted at the hospital level. Due to data limitations, very few studies have investigated the health care market concentration at the level of individual doctors. However, secondary data from an E-consultation website provided a unique opportunity to explore this important question at the individual doctor’s level.
Third, this study explores possible ways to decrease E-consultation market concentration from the information asymmetry perspective. Our findings reveal that two types of information disclosure mechanisms (ie, user feedback-based reputation and online self-representation) help to balance the supply and demand of health care service, which results in improved market efficiency.
This study has several limitations. First, only cross-sectional data were used in this study. Therefore, the role of intertemporal factors cannot be explored, and influences from many specific individual attributes cannot be completely eliminated. In the future, the panel data analysis method could be incorporated. Panel analysis uses panel data to examine changes in variables over time and differences in variables between subjects. The panel data contain rich information and would allow us to control for specific indicators. If the theory we proposed is correct and the data are sufficient, the results from panel analysis should be consistent with the cross-sectional analysis.
Second, data on only three disease types and from only one website (haodf.com) were used in this study. Therefore, the results of this study may not be fully representative of all diseases and the whole E-consultation market. In the future, we will continue this research by collecting data from multiple E-consultation websites and for more disease types.
Our findings suggest that the E-consultation market is more concentrated than the offline market, exhibiting a superstar effect. Meanwhile, concentration can be reduced if the doctor’s signals of quality are sent properly. A market served by many doctors with strong reputations or high levels of self-representation will be less concentrated.
These findings provide significant insights for E-consultation website designers as well as for policy makers. This research reveals that user feedback and online representation are two important mechanisms that E-consultation websites should provide and encourage. A possible and important way to reduce the market concentration of E-consultation services is to accumulate enough highly rated and highly self-represented doctors.
We intend to explore how the level of market concentration varies based on different conditions in the future. For example, how does level of concentration vary based on specific type of online services (eg, diagnosis, monitoring, or intervention services)? How does level of concentration vary based on different condition types (eg, acute vs chronic, high mortality vs low mortality, rare vs common, urgency vs non-urgency)? How does level of concentration vary based on the distribution of offline medical resources? Answering these research questions may help us better understand the impact of internet on health consultation market concentration.
Empirical model.
Robustness check.
This research was supported by the National Natural Science Foundation of China with grants 71371005, 71471064, and 71503108.
None declared.