A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community

Tingshuang Liao; Wei Qi; Biao Xiang

doi:doi:10.11648/j.sjbm.20221002.12

| Peer-Reviewed

A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community

Tingshuang Liao, Wei Qi, Biao Xiang

Published in Science Journal of Business and Management (Volume 10, Issue 2)

Received: 18 April 2022 Accepted: Published: 20 April 2022

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Online medical communities have bestially alleviated the traditional medical field problems that uneven distribution of medical resources and difficulty seeking medical treatment to a certain extent. Patients in the online medical community pay more attention to the doctors’ word-of-mouth (WOM), resulting in the doctors’ WOM effect. There are few studies on the influencing factors of doctors' WOM effect in online medical communities, hence this study has theoretical and practical application value. By integrating trust theory, social network theory, online reputation feedback mechanism and the research framework of WOM under the Internet, this study constructs factors’ four dimensions that have an impact on doctor's WOM effect, combined with control variables to construct influential factors of doctor's WOM effect in the online medical community. The corresponding indicators that affect the doctor's WOM effect were built and sorted by random forest regression with the doctor's online medical community data. This study performed random permutations and multiple regression based on the selected variables integrated with the control variables to obtain the optimal model. The multiple regression analysis illustrates doctor's WOM effect with control variables exploring the interaction effect. This study explores the optimal model that affects the doctors’ WOM effect in the online medical community and aims to reasonably guide the scientific operation of the online medical community platform.

Published in	Science Journal of Business and Management (Volume 10, Issue 2)
DOI	10.11648/j.sjbm.20221002.12
Page(s)	68-74
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Online Medical Community, WOM Effect, Random Forest Regression, Multiple Regression

References

[1]	Quan Fu, Research on Chinese Development of Internet Medical. Wuhan University, 2017.
[2]	Jang Wu and Li Shi, Study of the User Interaction Behavior in Online Health Community Based on Social Network Analysis, Information Science, 2017, 35 (07), pp. 120-125.
[3]	Wu Hong and Lu Naiji. Service provision, pricing, and patient satisfaction in online health communities, International journal of medical informatics, 2018, 110 (FEB.), pp. 77-89.
[4]	Willis and Erin, Patients’ self-efficacy within online health communities: facilitating chronic disease self-management behaviors through peer education, Health Communication, 2016, 31 (3), pp. 299-307.
[5]	Li X and Liu Z, Word of mouth effects in the investment of Chinese mutual fund managers, Management Review, 2012, 16 (11), pp. 1127-34.
[6]	Yang J, Kim W, Amblee N, et al, The heterogeneous effect of word of mouth on product sales: why the effect of WOM valence is mixed?, European Journal of Marketing, 2012, 46 (11-12), pp. 1523-1538.
[7]	Britt S. Consumer behavior and the behavioral sciences: theories and applications, Nouveaux Développements En Planétologie Dynamique, 1966.
[8]	Arndt J, Selective Processes in Word of Mouth, Journal of Advertising Research, 1968, 8 (3), pp. 19-22.
[9]	Elihu Katz, Paul F, Lazarsfeld and Elmo Roper, Personal Influence: The Part Played by People in the Flow of Mass Communications, Taylor and Francis, 2017.
[10]	Whyte W H, The web of word of mouth, Fortune, 1954, 50 (5), pp. 140-143.
[11]	Chatterjee P, Online reviews: do consumers use them? Advances in Consumer Research, 2001, 28 (1), pp. 129-133.
[12]	Jun Li. Attention to Trust Mechanism Promoting the Improvement of Doctor-patient Relationship, Journal of Branch Campus of the First Military Medical University, 2003 (01), pp. 39-40+61.
[13]	Harvir S, Bansal, Peter A and Voyer, Word of mouth Processes within a Services Purchase Decision Context, Journal of Service Research, 2000, 3 (2), pp. 166-177.
[14]	Lin Xu, An Empirical Analysis of Factors Influencing the Credibility of Online Word-of-Mouth, Finance and Trade Research, 2007 (05), pp. 113-117.
[15]	Bristor J M, Enhanced explanations of word of mouth communications: The power of relationships, Research in Consumer Behavior, 1990, 4 (1), pp. 51-83.
[16]	Elizabeth Sillence, Pam Briggs, Peter Richard Harris and Lesley Fishwick, How do patients evaluate and make use of online health information? Social Science & Medicine, 2007, 64 (9), pp. 1853-1862.
[17]	Dellarocas C, Reputation mechanisms, Handbook on Economics and Information Systems, 2006, pp. 629-660.
[18]	Jingjing Zhang, A Research on the Determinants of Re-diffusion Intention of Online Word-of-mouth, Zhejiang University, 2007.
[19]	Deutsch M, Trust and Suspicion, Journal of Conflict Resolution, 1958, 2 (4), pp. 265-279.
[20]	Lu B, Fan W and Zhou M, Social presence, trust, and social commerce purchase intention: An empirical research, Computers in Human Behavior, 2016, 56 (Mar.), pp. 225-237.
[21]	Ponte E B, Carvajal-Trujillo E and Escobar-Rodriguez T, Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents, Tourism Management, 2015, 47 (apr.), pp. 286-302.
[22]	Zhu Y and Chen H, Service fairness and customer satisfaction in internet banking, Internet Research, 2012, 22 (4), pp. 482-498 (17).
[23]	Ladhari R, Souiden N and Dufour B, The role of emotions in utilitarian service settings: The effects of emotional satisfaction on product perception and behavioral intentions, Journal of Retailing and Consumer Services, 2017, 34, pp. 10-18.
[24]	Granovetter M S, The Strength of Weak Ties, American Journal of Sociology, 1973, 78 (6), pp. 1360-1380.
[25]	Hai-hua Hu, Le Wang, Lining Jiang and Wei Yang, Strong ties versus weak ties in word of mouth marketing, BRQ Business Research Quarterly, 2019, 22 (4), pp. 245-256.
[26]	Coleman and James, Social Capital in the Creation of Human Capital, American Journal of Sociology, 1988, 94, pp. 95-120.
[27]	Paul Resnick, Ko Kuwabara, Richard Zeckhauser and Eric Friedman, Reputation systems, Communications of the ACM, 2000, 43 (12), pp. 45-48.
[28]	Gilly M C, Graham J L, Wolfinbarger M F, et al, A dyadic study of interpersonal information search, Journal of the Academy of Marketing Science, 1998, 26 (2), pp. 83-100.
[29]	Weihong Zhao, Building Customer Loyalty in Internet Online Retail: Focusing on the Linkages and Relative Effects of Value Perception, Satisfaction Experience, and Trust, Journal of Business Economics, 2007 (05), pp. 40-46.
[30]	Jinlun Han and Fuxiang Wei, Study on the Interactive Relationship Between Customer Satisfaction and Loyalty, Nankai Business Review, 2001 (06), pp. 8-10+29.
[31]	Zhuang Kang, Xiutai Yang and Yuanquan Yu, Study of the Relationship among the Customer Participation, Trust and Satisfaction, Journal of Zhongnan University of Economics and Law, 2009 (01), pp. 135-140.
[32]	Ratneshwar S and Chaiken S, Comprehension's Role in Persuasion: The Case of Its Moderating Effect on the Persuasive Impact of Source Cues, Journal of Consumer Research, 1991, 18 (1), pp. 52-62.
[33]	Xiaoxiao Liu, Study on the influence of online doctor reputation and doctor effort on consultation volume, Harbin Institute of Technology, 2014.
[34]	Xiaoting Xu, Mengqing Yang and Xiaokang Song, Exploring the Impact of Physicians' Word of Mouth on Patients' Selection in Online Health Community——Taking the Website of www.haodf.com as an Example, Modern Information, 2019, 39 (08), pp. 20-28+36.
[35]	Null R, Team R, Null R, et al, R: A language and environment for statistical computing, Computing, 2011, 1, pp. 12-21.

Cite This Article

Plain Text BibTeX RIS

APA Style

Tingshuang Liao, Wei Qi, Biao Xiang. (2022). A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community. Science Journal of Business and Management, 10(2), 68-74. https://doi.org/10.11648/j.sjbm.20221002.12

Copy | Download

ACS Style

Tingshuang Liao; Wei Qi; Biao Xiang. A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community. Sci. J. Bus. Manag. 2022, 10(2), 68-74. doi: 10.11648/j.sjbm.20221002.12

Copy | Download

AMA Style

Tingshuang Liao, Wei Qi, Biao Xiang. A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community. Sci J Bus Manag. 2022;10(2):68-74. doi: 10.11648/j.sjbm.20221002.12

Copy | Download

@article{10.11648/j.sjbm.20221002.12,
  author = {Tingshuang Liao and Wei Qi and Biao Xiang},
  title = {A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community},
  journal = {Science Journal of Business and Management},
  volume = {10},
  number = {2},
  pages = {68-74},
  doi = {10.11648/j.sjbm.20221002.12},
  url = {https://doi.org/10.11648/j.sjbm.20221002.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20221002.12},
  abstract = {Online medical communities have bestially alleviated the traditional medical field problems that uneven distribution of medical resources and difficulty seeking medical treatment to a certain extent. Patients in the online medical community pay more attention to the doctors’ word-of-mouth (WOM), resulting in the doctors’ WOM effect. There are few studies on the influencing factors of doctors' WOM effect in online medical communities, hence this study has theoretical and practical application value. By integrating trust theory, social network theory, online reputation feedback mechanism and the research framework of WOM under the Internet, this study constructs factors’ four dimensions that have an impact on doctor's WOM effect, combined with control variables to construct influential factors of doctor's WOM effect in the online medical community. The corresponding indicators that affect the doctor's WOM effect were built and sorted by random forest regression with the doctor's online medical community data. This study performed random permutations and multiple regression based on the selected variables integrated with the control variables to obtain the optimal model. The multiple regression analysis illustrates doctor's WOM effect with control variables exploring the interaction effect. This study explores the optimal model that affects the doctors’ WOM effect in the online medical community and aims to reasonably guide the scientific operation of the online medical community platform.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community
AU  - Tingshuang Liao
AU  - Wei Qi
AU  - Biao Xiang
Y1  - 2022/04/20
PY  - 2022
N1  - https://doi.org/10.11648/j.sjbm.20221002.12
DO  - 10.11648/j.sjbm.20221002.12
T2  - Science Journal of Business and Management
JF  - Science Journal of Business and Management
JO  - Science Journal of Business and Management
SP  - 68
EP  - 74
PB  - Science Publishing Group
SN  - 2331-0634
UR  - https://doi.org/10.11648/j.sjbm.20221002.12
AB  - Online medical communities have bestially alleviated the traditional medical field problems that uneven distribution of medical resources and difficulty seeking medical treatment to a certain extent. Patients in the online medical community pay more attention to the doctors’ word-of-mouth (WOM), resulting in the doctors’ WOM effect. There are few studies on the influencing factors of doctors' WOM effect in online medical communities, hence this study has theoretical and practical application value. By integrating trust theory, social network theory, online reputation feedback mechanism and the research framework of WOM under the Internet, this study constructs factors’ four dimensions that have an impact on doctor's WOM effect, combined with control variables to construct influential factors of doctor's WOM effect in the online medical community. The corresponding indicators that affect the doctor's WOM effect were built and sorted by random forest regression with the doctor's online medical community data. This study performed random permutations and multiple regression based on the selected variables integrated with the control variables to obtain the optimal model. The multiple regression analysis illustrates doctor's WOM effect with control variables exploring the interaction effect. This study explores the optimal model that affects the doctors’ WOM effect in the online medical community and aims to reasonably guide the scientific operation of the online medical community platform.
VL  - 10
IS  - 2
ER  -

Copy | Download

Author Information

Tingshuang Liao

School of Management Harbin Institute of Technology, Harbin Institute of Technology, Harbin, China
Wei Qi

School of Management Harbin Institute of Technology, Harbin Institute of Technology, Harbin, China
Biao Xiang

School of Management Harbin Institute of Technology, Harbin Institute of Technology, Harbin, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Tingshuang Liao, Wei Qi, Biao Xiang. (2022). A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community. Science Journal of Business and Management, 10(2), 68-74. https://doi.org/10.11648/j.sjbm.20221002.12

Copy | Download

ACS Style

Tingshuang Liao; Wei Qi; Biao Xiang. A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community. Sci. J. Bus. Manag. 2022, 10(2), 68-74. doi: 10.11648/j.sjbm.20221002.12

Copy | Download

AMA Style

Tingshuang Liao, Wei Qi, Biao Xiang. A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community. Sci J Bus Manag. 2022;10(2):68-74. doi: 10.11648/j.sjbm.20221002.12

Copy | Download

@article{10.11648/j.sjbm.20221002.12,
  author = {Tingshuang Liao and Wei Qi and Biao Xiang},
  title = {A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community},
  journal = {Science Journal of Business and Management},
  volume = {10},
  number = {2},
  pages = {68-74},
  doi = {10.11648/j.sjbm.20221002.12},
  url = {https://doi.org/10.11648/j.sjbm.20221002.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20221002.12},
  abstract = {Online medical communities have bestially alleviated the traditional medical field problems that uneven distribution of medical resources and difficulty seeking medical treatment to a certain extent. Patients in the online medical community pay more attention to the doctors’ word-of-mouth (WOM), resulting in the doctors’ WOM effect. There are few studies on the influencing factors of doctors' WOM effect in online medical communities, hence this study has theoretical and practical application value. By integrating trust theory, social network theory, online reputation feedback mechanism and the research framework of WOM under the Internet, this study constructs factors’ four dimensions that have an impact on doctor's WOM effect, combined with control variables to construct influential factors of doctor's WOM effect in the online medical community. The corresponding indicators that affect the doctor's WOM effect were built and sorted by random forest regression with the doctor's online medical community data. This study performed random permutations and multiple regression based on the selected variables integrated with the control variables to obtain the optimal model. The multiple regression analysis illustrates doctor's WOM effect with control variables exploring the interaction effect. This study explores the optimal model that affects the doctors’ WOM effect in the online medical community and aims to reasonably guide the scientific operation of the online medical community platform.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - A Study of Influential Factors in Doctor's WOM Effect Within Online Medical Community
AU  - Tingshuang Liao
AU  - Wei Qi
AU  - Biao Xiang
Y1  - 2022/04/20
PY  - 2022
N1  - https://doi.org/10.11648/j.sjbm.20221002.12
DO  - 10.11648/j.sjbm.20221002.12
T2  - Science Journal of Business and Management
JF  - Science Journal of Business and Management
JO  - Science Journal of Business and Management
SP  - 68
EP  - 74
PB  - Science Publishing Group
SN  - 2331-0634
UR  - https://doi.org/10.11648/j.sjbm.20221002.12
AB  - Online medical communities have bestially alleviated the traditional medical field problems that uneven distribution of medical resources and difficulty seeking medical treatment to a certain extent. Patients in the online medical community pay more attention to the doctors’ word-of-mouth (WOM), resulting in the doctors’ WOM effect. There are few studies on the influencing factors of doctors' WOM effect in online medical communities, hence this study has theoretical and practical application value. By integrating trust theory, social network theory, online reputation feedback mechanism and the research framework of WOM under the Internet, this study constructs factors’ four dimensions that have an impact on doctor's WOM effect, combined with control variables to construct influential factors of doctor's WOM effect in the online medical community. The corresponding indicators that affect the doctor's WOM effect were built and sorted by random forest regression with the doctor's online medical community data. This study performed random permutations and multiple regression based on the selected variables integrated with the control variables to obtain the optimal model. The multiple regression analysis illustrates doctor's WOM effect with control variables exploring the interaction effect. This study explores the optimal model that affects the doctors’ WOM effect in the online medical community and aims to reasonably guide the scientific operation of the online medical community platform.
VL  - 10
IS  - 2
ER  -

Copy | Download