Examining Online Health Community Users’ Information Adoption Intention

Due to the doubt on information credibility, users often hesitate to adopt the health information posted on online health communities (OHC). This may undermine the sustainable development of OHC. The purpose of this research is to identify the determinants of OHC users’ information adoption intention. The results indicated that both information factors and social interaction affect the adoption intention. Information factors include argument quality and source credibility, whereas social interaction includes perceived similarity and familiarity. The results imply that OHC need to ensure information quality and support users’ interaction in order to facilitate their information adoption.

Download Full-text

q-Rung orthopair fuzzy TOPSIS method and the application to information service quality evaluation in online health community

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211123 ◽

2021 ◽

pp. 1-18

Author(s):

Aihui Ye ◽

Runtong Zhang ◽

Pei Wu ◽

Yuping Xing

Keyword(s):

Information Quality ◽

Quality Evaluation ◽

Evaluation Criteria ◽

Information Service ◽

Topsis Method ◽

Online Health Communities ◽

Online Health Community ◽

Health Community ◽

Pca Method ◽

Health Communities

Since the information quality in the online health community is very important for users to obtain valuable health information, information quality evaluation is a necessary research that involves a multi-attribute decision-making (MADM) problem. However, few researches have been done to address both the construction of evaluation criteria and the expression and processing of fuzzy information, especially in online health community. This manuscript proposes a novel evaluation framework of information service quality combined principal component analysis (PCA) method with the TOPSIS method under q-rung orthopair fuzzy set (q-ROFS) environment. An accurate evaluation criteria system is optimized by the PCA method, and the q-ROF TOPSIS method is proposed to process larger space of fuzzy evaluation information and overcome information loss and information distortion, in which a new distance measure between q-ROFSs is defined and an entropy weight model is initiated to determine the unknown weight of attribute. Moreover, a numerical example is performed to prove the practicability and superiority of the method through comparative analysis, which gives clear results of information quality evaluation of four online health communities. This research ends with fuzzy decision-making theory and application, and provides references for standardizing and improving the information quality of online health communities.

Download Full-text

Understanding online health community users' information adoption intention: an elaboration likelihood model perspective

Online Information Review ◽

10.1108/oir-09-2020-0412 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Tao Zhou

Keyword(s):

User Behavior ◽

Source Credibility ◽

Elaboration Likelihood Model ◽

Elaboration Likelihood ◽

Content Type ◽

Peripheral Cues ◽

Likelihood Model ◽

Adoption Intention ◽

Information Adoption ◽

Central Cues

PurposeThe purpose of this research is to draw on the elaboration likelihood model (ELM) to examine users' information adoption intention in online health communities (OHC).Design/methodology/approachThe authors collected 350 valid responses using a survey and conducted the moderated regression analysis to examine the research model.FindingsThe results indicated that users' information adoption intention is influenced by both central cues (argument quality) and peripheral cues (source credibility and emotional support). In addition, self-efficacy moderates the effect of both central cues and peripheral cues on information adoption intention.Originality/valuePrevious research has focused on the effect of individual motivations such as reciprocity and benefits on user behavior, and has seldom disclosed the influencing process of external factors on OHC users' behavioral decision. This research tries to fill the gap by adopting ELM to uncover the mechanism underlying OHC users' information adoption.

Download Full-text

Why Do Users of Online Mental Health Communities Get Likes and Reposts: A Combination of Text Mining and Empirical Analysis

Healthcare ◽

10.3390/healthcare9091133 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1133

Author(s):

Jingfang Liu ◽

Jun Kong

Keyword(s):

Mental Health ◽

Text Mining ◽

Mental Disorders ◽

Empirical Analysis ◽

Online Community ◽

Information Quality ◽

Social Experience ◽

Adoption Model ◽

Health Communities ◽

Information Adoption

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

Download Full-text

Understanding the Factors Influencing Patient E-Health Literacy in Online Health Communities (OHCs): A Social Cognitive Theory Perspective

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16142455 ◽

2019 ◽

Vol 16 (14) ◽

pp. 2455 ◽

Cited By ~ 9

Author(s):

Junjie Zhou ◽

Tingting Fan

Keyword(s):

Social Interaction ◽

Health Literacy ◽

Social Cognitive ◽

Cognitive Theory ◽

Health Knowledge ◽

Online Health Communities ◽

Factors Influencing ◽

Health Communities ◽

Social Environmental ◽

Knowledge Seeking

Although online health communities (OHCs) are increasingly popular in public health promotion, few studies have explored the factors influencing patient e-health literacy in OHCs. This paper aims to address the above gap. Based on social cognitive theory, we identified one behavioral factor (i.e., health knowledge seeking) and one social environmental factor (i.e., social interaction ties) and proposed that both health knowledge seeking and social interaction ties directly influence patient e-health literacy; in addition, social interaction ties positively moderate the effect of health knowledge seeking on patient e-health literacy. We collected 333 valid data points and verified our three hypotheses. The empirical results provide two crucial findings. First, both health knowledge seeking and social interaction ties positively influence patient e-health literacy in OHCs. Second, social interaction ties positively moderate the effect of health knowledge seeking on patient e-health literacy. These findings firstly contribute to public health literature by exploring the mechanism of how different factors influence patient e-health literacy in OHCs and further contribute to e-health literacy literature by verifying the impact of social environmental factors.

Download Full-text

Impact of Patient Crowdvotes Aggregated Service Diversity on Doctors� Service Sales in Online Health Community: a Retrospective Study (Preprint)

10.2196/preprints.11063 ◽

2018 ◽

Author(s):

Hai-Yan Yu ◽

Jying-Nan Wang ◽

Ya-Ling Chiu ◽

Hang Qiu ◽

Ling Xiao

Keyword(s):

Retrospective Study ◽

Good Doctor ◽

Lorenz Curves ◽

Online Health Communities ◽

Online Health Community ◽

Health Community ◽

Main Effect ◽

Health Communities ◽

Log Linear ◽

Disease Understanding

BACKGROUND An increasing number of people visit online health communities to esquire health information with doctors. In the online health community (OHC), patient crowds tended to label and vote the doctors’ specialties with encountered disease. Understanding how patients’ online labels can help us understand the service diversity for patients in online health communities and provide constructive suggestions for doctors serving more patients online. OBJECTIVE Our goal was to understand: (1) what kind of patterns are the labels of patient crowdvotes aggregated service diversity, including encountered disease labels and online votes, in a OHC? (2) wheather the patient crowdvotes aggregated service diversity make doctors’ service sales difference in OHC? (3) how can managers in OHC perform to improve doctors’ service sales with the feedback of crowdvotes aggregated service diversity? METHODS We designed a retrospective study with data collected from the largest OHC (Good Doctor website) in China. We first used descriptive statistics to investigate the patient crowdvotes aggregated service diversity. Then a multiple log-linear relationship was adapted to investigate the main and the interaction impact of service diversity on doctors’ service sales. RESULTS Our sample consists of 9,841 doctors from 1,255 different hospitals widely distributed in China. 18,997,018 patients had been serviced by these doctors since they became members of the study OHC. 704,467 votes of doctors’ clinical specialties were labeled by patient crowds in recent two years (Aug.26, 2015-Aug. 25, 2017). Gini coefficient of serviced patients is very high, 0.626, followed by the volume of votes (0.562). Based on the regression model, we found that the coefficients of the control variables, doctor review rating and clinic title, were 0.810(0.041), and 1.735 (0.027), respectively. For the breadth of voted specialties, volume of votes and degree of voted diversity, the standardized coefficient of the main effect were 0.309 (0.038), 0.745 (0.014) and 0.073 (0.018), respectively. All of the estimates are statistically significant at a 0.1% level. CONCLUSIONS Our study provided empirical evidence that the patterns of both the labels of patient crowdvotes aggregated service diversity and doctors’ service sales were of inequality (as illustrated in Lorenz curves) in the distribution of its size of serviced patients in a OHC. Patient crowds’ online labels also leaded to differences in the doctors’ service sales online. The treads of the doctors’ service sales kept increasing as the patient crowdvotes aggregated service diversity increased. Finally, our findings suggested that the higher breadth of voted specialties and degree of voted diversity displayed a greater service sales with a higher review rating, deploying less inequality of Doctors’ service sales.

Download Full-text

Social exchange approach in knowledge sharing of online communities – A Case of online health communities at Ho Chi Minh City

Science & Technology Development Journal - Economics - Law and Management ◽

10.32508/stdjelm.v1iq4.471 ◽

2017 ◽

Vol 1 (Q4) ◽

pp. 13-24

Author(s):

Phong Thanh Nguyen ◽

Tuan Manh Nguyen

Keyword(s):

Social Exchange ◽

Positive Impact ◽

Sense Of Self ◽

Online Health Communities ◽

Online Health Community ◽

Writing Group ◽

Health Community ◽

Self Worth ◽

Face Concern ◽

Health Communities

The demand to look for information and share information in nowaday society are a huge needed, especially in the internet revolution are developing more and more. The studies proposed the model that includes the benefit factors (sense of self-worth, face concern, reputation and social support) and cost factors (executional costs, cognitive costs) with the points of view of Social Exchange Theory that influences to knowledge donating behavior, knowledge collecting behavior and community promotion among members. The studies will be verified in health care member of the online health communities in Ho Chi Minh City. Quantitative research also was conducted 336 samples were used to evaluate and test the research. The results of the Structural Equation Modeling (SEM) show that the theoretical models are suited the market data and hypotheses of the research model are supported. In particular, factors of the benifit group (sense of self-worth, face concern, reputation and social support) have a positive impact on the knowledge donating behavior and knowledge collecting behavior. In addition, factors of the cost group (executional costs, cognitive costs) have a negative impact the knowledge donating behavior and knowledge collecting behavior. Knowledge donating behavior and knowledge collecting behavior have a positive impact on community promotion to the online health community. In addition, the results of multi-group analysis that there is no difference between knowledge sharing’s writing group and no knowledge sharing’s writing group. The results may be usefull for online health community, hospitals, doctors, individuals and businesses.

Download Full-text

Study on the Evolution of Information Sharing Strategy for Users of Medical Cloud

10.21203/rs.2.19245/v1 ◽

2019 ◽

Author(s):

Panpan Zhu ◽

Jiang Shen ◽

Man Xu

Keyword(s):

Information Sharing ◽

Scale Free ◽

Online Health Communities ◽

Online Health Community ◽

Health Community ◽

Community Information ◽

Free Network ◽

Health Communities ◽

Sharing Behavior ◽

Community Patients

Abstract Technological advances are driving the growth of online health communities. However, there are some problems such as low user participation and insignificant social benefits in online health communities. This paper discusses the evolution law of information sharing behavior of members of online health community to study the influence of different behaviors on health information sharing results and explore the ways to improve the level of community information sharing. Based on BA scale-free network (Albert-László Barabás and Réka Albert scale-free network) , this paper established an information sharing behavior model for members of online health community with the evolutionary game theory method, and discussed the influence of different game parameters and initial conditions on the evolution results of information sharing behavior of community patients with the method of numerical experiment.Results: It is found that the key to improve the level of community information sharing is to improve the benefit of patients' information sharing, the proportion of patients sharing information at the initial moment, the degree of network nodes, and reduce the sharing cost. Community managers should improve the information conversion ability and information absorption ability of community patients through offline activities, professional guidance and other forms. At the same time, it can reduce the difficulty and risk of information sharing and strengthen the connection among members, thus comprehensively enhancing the value of the community.

Download Full-text

Examining users’ knowledge sharing behaviour in online health communities

Data Technologies and Applications ◽

10.1108/dta-03-2019-0044 ◽

2019 ◽

Vol 53 (4) ◽

pp. 442-455 ◽

Cited By ~ 4

Author(s):

Tao Zhou

Keyword(s):

Knowledge Sharing ◽

Structural Equation ◽

Information Quality ◽

Service Providers ◽

Research Model ◽

Privacy Concern ◽

Content Type ◽

Online Health Communities ◽

Community Platforms ◽

Health Communities

Purpose The purpose of this paper is to draw on the stimulus-organism-response (SOR) framework to examine users’ knowledge sharing in online health communities (OHC). Design/methodology/approach Based on the 326 valid responses collected from a survey, structural equation modelling was employed to examine the research model. Findings The results indicated that both information quality and service quality affect trust in community, whereas both informational support and emotional support affect trust in other members. Both types of trust and privacy risk determine users’ sharing intention, which in turn affects sharing behaviour. Research limitations/implications The results imply that service providers need to improve their community platforms and create a supportive climate in order to facilitate users’ trust and their knowledge sharing behaviour. Originality/value Previous studies have examined a few determinants of OHC user behaviour such as privacy concern, trust and motivations. However, they have seldom disclosed the internal decision process underlying users’ knowledge sharing. This research tries to fill the gap.

Download Full-text

Development and Evaluation of the Canteen Connect Online Health Community: Using a Participatory Design Approach in Meeting the Needs of Young People Impacted by Cancer

Cancers ◽

10.3390/cancers14010050 ◽

2021 ◽

Vol 14 (1) ◽

pp. 50

Author(s):

Jennifer Cohen ◽

Pandora Patterson ◽

Melissa Noke ◽

Kristina Clarke ◽

Olga Husson

Keyword(s):

Participatory Design ◽

Positive Impact ◽

Familial Cancer ◽

Online Health Communities ◽

Implementation Evaluation ◽

Online Health Community ◽

Health Community ◽

Health Related ◽

Adolescent And Young Adults ◽

Health Communities

Adolescent and young adults (AYAs) impacted by their own or familial cancer require information and peer support throughout the cancer journey to ameliorate feelings of isolation. Online Health Communities (OHC) provide social networks, support, and health-related content to people united by a shared health experience. Using a participatory design (PD) process, Canteen developed Canteen Connect (CC), an OHC for AYAs impacted by cancer. This manuscript outlines the process used to develop CC: (1) A mixed-methods implementation evaluation of Version I of CC (CCv.1); (2) Qualitative workshops utilizing strengths-based approaches of PD and appreciative inquiry to inform the development of CC Version 2 (CCv.2); quantitative implementation evaluation to assess the appropriateness, acceptability, and effectiveness of CCv.2. Through several iterations designed and tested in collaboration with AYAs, CCv.2 had improvements in the user experience, such as the ability to send a private message to other users and the site becoming mobile responsive. Results from the evaluation showed CCv.2 was appropriate for connecting with other AYAs. Most AYAs reported satisfaction with CCv.2 and a positive impact on their feelings of sadness, worry, and/or anxiety. CCv.2 fills an important service provision gap in providing an appropriate and acceptable OHC for AYAs impacted by cancer, with initial promising psychological outcomes.

Download Full-text

Exploring the Factors Influencing Consumers to Voluntarily Reward Free Health Service Contributors in Online Health Communities: Empirical Study (Preprint)

10.2196/preprints.16526 ◽

2019 ◽

Author(s):

Junjie Zhou ◽

Fang Liu ◽

Tingting Zhou

Keyword(s):

Social Interaction ◽

Health Service ◽

Emotional Support ◽

Social Norm ◽

Health Knowledge ◽

Informational Support ◽

Online Health Communities ◽

Factors Influencing ◽

Norm Compliance ◽

Health Communities

BACKGROUND Rewarding health knowledge and health service contributors with money is one possible approach for the sustainable provision of health knowledge and health services in online health communities (OHCs); however, the reasons why consumers voluntarily reward free health knowledge and health service contributors are still underinvestigated. OBJECTIVE This study aimed to address the abovementioned gap by exploring the factors influencing consumers’ voluntary rewarding behaviors (VRBs) toward contributors of free health services in OHCs. METHODS On the basis of prior studies and the cognitive-experiential self-theory (CEST), we incorporated two health service content–related variables (ie, informational support and emotional support) and two interpersonal factors (ie, social norm compliance and social interaction) and built a proposed model. We crawled a dataset from a Chinese OHC for mental health, coded it, extracted nine variables, and tested the model with a negative binomial model. RESULTS The data sample included 2148 health-related questions and 12,133 answers. The empirical results indicated that the effects of informational support (β=.168; P<.001), emotional support (β=.463; P<.001), social norm compliance (β=.510; P<.001), and social interaction (β=.281; P<.001) were significant. The moderating effects of social interaction on informational support (β=.032; P=.02) and emotional support (β=−.086; P<.001) were significant. The moderating effect of social interaction on social norm compliance (β=.014; P=.38) was insignificant. CONCLUSIONS Informational support, emotional support, social norm compliance, and social interaction positively influence consumers to voluntarily reward free online health service contributors. Social interaction enhances the effect of informational support but weakens the effect of emotional support. This study contributes to the literature on knowledge sharing in OHCs by exploring the factors influencing consumers’ VRBs toward free online health service contributors and contributes to the CEST literature by verifying that the effects of experiential and rational systems on individual behaviors can vary while external factors change.

Download Full-text