Investigating the effect of online and offline reputation on the provision of online counseling services: A case study of the Internet hospitals in China
Ronghua XU1(), Tingting ZHANG1, Qingpeng ZHANG2
1. School of Management, Zhejiang University of Technology, Hangzhou 310014, China 2. School of Data Science, City University of Hong Kong, Hong Kong, China
The sustainable development of Internet hospitals and e-health platforms relies on the participation of patients and physicians, especially on the provision of health counseling services by physicians. The objective of our study is to explore the factors motivating Chinese physicians to provide online health counseling services from the perspectives of their online and offline reputation. We collect the data of 141029 physicians from 6173 offline hospitals located in 350 cities in China. Based on the reputation theory and previous studies, we incorporate patients’ feedback as physicians’ online reputation and incorporate physicians’ offline professional status as physicians’ offline reputation. Results show that physicians’ online reputation significantly and positively influence their online counseling behaviors, whereas physicians’ offline reputation significantly and negatively influence their online counseling behaviors. We conclude that physician’s online and offline reputations show a competitive and substitute relationship rather than a complementary relationship in influencing physicians to provide online counseling services in Internet hospitals. One possible explanation for the substitute relationship could be the constraints of limited time and effort of physicians.
. [J]. Frontiers of Engineering Management, 2022, 9(4): 563-576.
Ronghua XU, Tingting ZHANG, Qingpeng ZHANG. Investigating the effect of online and offline reputation on the provision of online counseling services: A case study of the Internet hospitals in China. Front. Eng, 2022, 9(4): 563-576.
Physicians’ activeness of providing online services
Self-efficacy/Self-empowering ★ (Chen et al., 2020; Zhang et al., 2020; Yang et al., 2021)Social interaction ▲ (Lin and Chang, 2018)Physician–patient relationship and trust ▲★ (Chen et al., 2020; Yang et al., 2021)Reciprocity and altruism ▲ (Zhang et al., 2017; Chen et al., 2020)
Professional status Δ (National Health Commission of PRC, 2009; Chen et al., 2020)Extrinsic reward and return (e.g., monetary reward and consultation price) ☆ (Zhang et al., 2020; Chen et al., 2020)
Patients’ selection of online physicians
Perceived risk ★ (Li et al., 2020a)Perceived usefulness ★ (Shmueli and Koppius, 2011)Physician–patient relationship and trust ▲★ (Robin DiMatteo et al., 1979)
Hospital rankings ΔPhysicians’ medical titles Δ
Tab.1
Fig.1
Fig.2
Variables
Explanation
Dependent variable
Online consultation volume
The online consultation volume is the number of online conversations between the patients and the physicians. For example, the consultation volume of the physician in Fig.2 is 2339, meaning that he has conducted 2339 pieces of counseling conversations with patients online
Independent variable
Professional title
The professional titles include senior titles, associate senior titles, intermediate titles, junior titles, and non-titles, which are assigned values of 4, 3, 2, 1, and 0, respectively
Hospital ranking
Hospitals have several rankings, including ranking I (ranking IA, IB, and IC), ranking II (ranking IIA, IIB, and IIC), and ranking III (ranking IIIA, IIIB, and IIIC). Ranking III is better than ranking II, and ranking II is better than ranking I, which are encoded as 3, 2, and 1, respectively. Hospitals with no ranking are denoted as 0
City level
According to the 2019 City Business Charm Ranking distributed by YiMagazine on May 20, 2019, the city levels are divided into first-tier cities, new first-tier cities, second-tier cities, third-tier cities, fourth-tier cities, fifth-tier cities, and unlisted cities, encoded as 6, 5, 4, 3, 2, 1, and 0, respectively (Mao and Che, 2019)
Number of patients’ comment
This number contains all the textual comments written by the patients for the specific physician. For example, the number is 2368 in Fig.2, meaning that 2368 pieces of textual comments have been written by the patients
Satisfaction score
The satisfaction score is an average rating given by the patients after online consultations on the platform, ranging from 0 (null values) to 10 (indicating 100% satisfied)
Number of following patients
This number refers to those patients who have been following the physician for future possible consultations, either online or offline. For example, the number is 3866 in Fig.2, meaning that there have been 3866 patients who are following this physician online
Tab.2
Variables
Min
Max
Mean
Standard deviation
Online consultation volume
0
37000
22.11
373.98
Professional title
0
4
2.86
1.13
Hospital ranking
0
3
2.74
0.56
City level
0
6
4.31
1.43
Number of patients’ comment
0
14081
26.81
188.91
Satisfaction score
0
10.00
1.73
3.67
Number of following patients
0
20509
56.36
341.76
Tab.3
Fig.3
Variable
1
2
3
4
5
6
7
1 Online consultation volume
1
2 Professional title
−0.15**
1
3 Hospital ranking
−0.23**
0.39**
1
4 City level
−0.19**
0.38**
0.42**
1
5 Number of patients’ comments
0.57**
−0.004
−0.07**
0.03
1
6 Satisfaction score
0.06**
−0.08**
0.009
−0.05**
0.04**
1
7 Number of following patients
0.18**
0.25**
0.09**
0.25**
0.54**
−0.05**
1
Tab.4
Step 1
Step 2
Model 1
Model 2
FollowingPatients*SatisfactionScore
0.025
FollowingPatients*PatientsComments
−0.216***
CityLevel*ProfessionalTitle
−0.096
CityLevel*HospitalRanking
−0.177***
Satisfaction score
0.034**
0.030**
0.024*
Professional title
−0.084***
−0.049***
−0.073***
Hospital ranking
−0.163***
−0.052**
−0.153***
Number of patients’ comments
0.558***
0.564***
0.679***
Adjusted R-square
0.371
0.385
0.402
Durbin–Watson (DW) statistics
1.756
1.792
1.760
Tab.5
Fig.4
Criterion value
DW statistics
Adjusted R-square
20
1.766
0.361
35
1.774
0.369
50
1.756
0.371
65
1.722
0.367
80
1.669
0.365
Tab.6
Under criterion value of 20a)
Under criterion value of 35b)
Step 1
Step 2
Step 1
Step 2
Model 1
Model 2
Model 1
Model 2
FollowingPatients*SatisfactionScore
0.081***
0.049**
FollowingPatients*PatientsComments
−0.264***
−0.249***
CityLevel*ProfessionalTitle
−0.029
−0.019
CityLevel*HospitalRanking
−0.104***
−0.138***
Satisfaction score
0.098***
0.097***
0.089***
0.060***
0.058***
0.051***
Professional title
−0.072***
−0.054***
−0.078***
−0.076***
−0.051***
−0.076***
Hospital ranking
−0.136***
−0.071***
−0.126***
−0.156***
−0.069***
−0.145***
Number of patients’ comments
0.549***
0.553***
0.651***
0.557***
0.562***
0.670***
Adjusted R-square
0.361
0.367
0.396
0.369
0.378
0.403
DW statistics
1.766
1.780
1.766
1.774
1.797
1.778
Under criterion value of 65c)
Under criterion value of 80d)
Step 1
Step 2
Step 1
Step 2
Model 1
Model 2
Model 1
Model 2
FollowingPatients*SatisfactionScore
0.004
0.008
FollowingPatients*PatientsComments
−0.216***
−0.208***
CityLevel*ProfessionalTitle
−0.163*
−0.325***
CityLevel*HospitalRanking
−0.091*
−0.092
Satisfaction score
0.017
0.013
0.006
0
−0.004
−0.011
Professional title
−0.096***
−0.056
−0.083***
−0.091***
0.172***
−0.079***
Hospital ranking
−0.159***
−0.080**
−0.150***
−0.155***
−0.112***
−0.147***
Number of patients’ comments
0.557***
0.567***
0.678***
0.559***
0.571***
0.674***
Adjusted R-square
0.367
0.386
0.399
0.365
0.389
0.394
DW statistics
1.722
1.766
1.727
1.669
1.724
1.673
Tab.7
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