Examining online health community users’ sharing behaviour: A social influence perspective

2021 ◽  
pp. 026666692110071
Author(s):  
Tao Zhou
Keyword(s):  
Social Influence ◽  
Influence Factors ◽  
Privacy Concern ◽  
Social Influence Theory ◽  
Online Health Communities ◽  
Related Information ◽  
Health Community ◽  
The Social ◽  
Health Related ◽  
Health Communities

Online health communities (OHC) provide a platform for users to exchange health-related information and seek emotional support. However, users often lack the intention to share their knowledge, which may lead to the failure of OHC. Drawing on the social influence theory, this research examined OHC users’ sharing behaviour. The results indicated that users’ sharing intention is influenced by three social influence factors, which include subjective norm, social identity and group norm. In addition, social support and privacy concern have effects on these three social influence factors. The results imply that OHC need to leverage social influence in order to facilitate users’ sharing behaviour.

scholarly journals 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 ◽  
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.

The Power of Peers: Applying User-Generated Content to Health Behaviors “Off-Line”

2018 ◽  
Vol 28 (13) ◽  
pp. 2081-2093 ◽  
Author(s):  
Erin Willis
Keyword(s):  
Social Cognitive ◽  
Cognitive Theory ◽  
Management Strategies ◽  
Disease Diagnosis ◽  
Self Management ◽  
Online Health Communities ◽  
Shared Content ◽  
Health Community ◽  
The Social ◽  
Health Communities

Many patients seek and share information online regarding health, especially those diagnosed with chronic disease. The social cognitive theory is used as the theoretical framework for analyzing how members learn within online health communities. This study conducted in-depth interviews with members from online health communities related to arthritis to understand their motivation for participating in the community and how the content exchanged therein is applied to managing their disease. Four themes were identified: processing disease diagnosis, collaborating to solve problem, recognizing personal limitations, and appreciating that health is variable. Topical communication within the online communities was often tailored to members’ situational needs with the ultimate goal being better self-management. The findings demonstrate the online health community is an environment that invites members who share common characteristics to engage with one another and with the shared content for the purpose of learning arthritis self-management strategies. Theoretical and practical implications are discussed.

scholarly journals IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1469 ◽  
Author(s):  
Pradeepa Sampath ◽  
Gayathiri Packiriswamy ◽  
Nishmitha Pradeep Kumar ◽  
Vimal Shanmuganathan ◽  
Oh-Young Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Vital Role ◽  
Online Health Communities ◽  
Health Community ◽  
Chi Squared ◽  
Health Related ◽  
Health Communities ◽  
Processing Techniques

The unprompted patient’s and inimitable physician’s experience shared on online health communities (OHCs) contain a wealth of unexploited knowledge. Med Help and eHealth are some of the online health communities offering new insights and solutions to all health issues. Diabetes mellitus (DM), thyroid disorders and tuberculosis (TB) are chronic diseases increasing rapidly every year. As part of the project described in this article comments related to the diseases from Med Help were collected. The comments contain the patient and doctor discussions in an unstructured format. The sematic vision of the internet of things (IoT) plays a vital role in organizing the collected data. We pre-processed the data using standard natural language processing techniques and extracted the essential features of the words using the chi-squared test. After preprocessing the documents, we clustered them using the K-means++ algorithm, which is a popular centroid-based unsupervised iterative machine learning algorithm. A generative probabilistic model (LDA) was used to identify the essential topic in each cluster. This type of framework will empower the patients and doctors to identify the similarity and dissimilarity about the various diseases and important keywords among the diseases in the form of symptoms, medical tests and habits.

scholarly journals Understanding the Usage of Whatsapp Among Malaysian Employees Through the Lens of Social Influence Theory

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1039
Author(s):  
Che Su Mustaffa ◽  
Che Hasniza Che Soh ◽  
Hassan Abu Bakar ◽  
Bahtiar Mohamad
Keyword(s):  
Social Influence ◽  
Social Factors ◽  
Influence Factors ◽  
Sampling Technique ◽  
Focus Group Discussions ◽  
Group Discussions ◽  
Social Influence Theory ◽  
Routine Work ◽  
The Social ◽  
Depth Interviews

The study examines the social influence factors that affect the intention for using WhatsApp application among employees in a Malaysian organisation.  The objectives of the study are to ascertain the reasons for the employees to currently use WhatsApp and to examine the factors that affect the use of WhatsApp based on Social Influence Theory perspective. The nature of the study’s methodological design was qualitative. The main instruments for data collection were focus group discussions and in-depth interviews. Purposive sampling technique was adopted to ensure that the employees chosen had the relevant knowledge about WhatsApp. The findings indicated that compliance, internalization and identification were three important social factors that could influence the employees to use WhatsApp in their routine work as predicted by Social Influence Theory. This study contributes some insights regarding the factors that can contribute to the usage of social media and strengthen the ideas of Social Influence Theory in Malaysian context.  

Exploring the collective knowledge curation process of online health communities

2016 ◽  
Author(s):  
◽  
Wanli Xing
Keyword(s):  
Social Support ◽  
Information Flow ◽  
Flow Patterns ◽  
University Of Missouri ◽  
Online Health Communities ◽  
Health Community ◽  
Health Related ◽  
Health Communities ◽  
The University ◽  
Relationship Of

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] More and more people turn to online health communities for social support to satisfy their health-related needs. Previous studies on social support and online health communities in general have focused on the content of social support and the relationship of social support with other entities using traditional social science methods. Little is known about how social support facilitate the knowledge curation process in an online health community. Moreover, the presence of misinformation in online health communities also calls for research into the knowledge curation process in order to reduce the risk of misinformation. This study uses data mining technologies to analyze around one million posts across 23 online health communities. It aims to reveal how information, through social support, flows between the community users working as a whole to dynamically curate knowledge and further interacts with information accuracy. This data-centric research in online health communities 1) discovered that xperiphery users instead of core users dominate the quantitative and content information flow; 2) identified three temporal information flow patterns for the knowledge curation process -- each with distinct characteristics; 3) found that information accuracy differed significantly over the identified information flow patterns and time and the information accuracy variation trends with each information flow pattern was identified as well. These findings not only have important implications for social support use, delivery and social support research methodologies but also can inform future online health platform design.

scholarly journals Knowledge sharing in online smoking cessation communities: a social capital perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chenglong Li ◽  
Hongxiu Li ◽  
Reima Suomi ◽  
Yong Liu
Keyword(s):  
Social Capital ◽  
Smoking Cessation ◽  
Knowledge Sharing ◽  
Content Type ◽  
Online Health Communities ◽  
Structural Capital ◽  
The Social ◽  
Health Communities ◽  
Social Capital Perspective ◽  
Practical Implications

PurposeAlthough knowledge sharing in online communities has been studied for many years, little is known about the determinants for individuals' knowledge sharing in online health communities (OHCs) surrounding smoking cessation. Examining the determinants of knowledge sharing in such OHCs from the social capital perspective may prove particularly enlightening.Design/methodology/approachA questionnaire-based online user survey of two smoking cessation OHCs, one based in Finland and one based in China, was performed. Performing data analysis with partial least squares (SmartPLS 3.0), the authors developed a model conceptualizing the structural, cognitive and relational dimensions of social capital as drivers for knowledge sharing in smoking cessation OHCs, with users' stage in giving up smoking as a moderator.FindingsThe results show that structural capital (social ties) and relational capital (reciprocity) are important motivators behind knowledge sharing in smoking cessation OHCs, and the authors found a moderating effect of the stage in quitting on the antecedents' relationship with knowledge sharing in these OHCs.Originality/valueThe study enriches understanding of knowledge sharing in smoking cessation OHCs, contributing to theory and identifying practical implications for such groups' administration.

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

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.

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