Understanding Members’ Active Participation in Online Question-and-Answer Communities: A Theory and Empirical Analysis

Khansa, Lara; Ma, Xiao; Liginlal, Divakaran; Kim, Sung S.

doi:10.1080/07421222.2015.1063293

Cited by 130 publications

(113 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Tables 2 and 3, a higher POC t or DR t leaded to more online contribution in the next month. These results were consistent with those of prior literature, i.e., people's past contribution was highly correlated with their subsequent contribution [15,34], and the review rating was a driver for online participation [26]. The other three control variables were related to physicians' offline status.…”

Section: Principal Resultssupporting

confidence: 82%

See 1 more Smart Citation

Understanding a Nonlinear Causal Relationship Between Rewards and Physicians� Contributions in Online Health Care Communities: Longitudinal Study (Preprint)

Wang¹,

Chiu²,

Yu³

et al. 2017

Preprint

View full text Add to dashboard Cite

Background: The online health-care community (OHC) is not just a place for the public to share physician reviews or medical knowledge; it is also a physician-patient communication platform. The medical resources of developing countries are relatively inadequate, and the OHC is a potential solution to alleviate the phenomenon of long hospital queues and the lack of medical resources in rural areas. However, the success of the OHC depends on online contributions by physicians. Objective: The aim of this study is not only to understand physician online contribution behavior, but also to examine the helpfulness of incentive mechanisms. We addressed the following questions: (1) From which specialty area are physicians more likely to participate in OHC activities? (2) What are the factors affecting physician online contributions? (3) Do incentive mechanisms, including psychological and material rewards, result in differences of physician online contributions? (4) How can OHC managers exploit feasible incentive mechanisms to improve physician online contributions? Methods: We designed a longitudinal study involving a data sample in three waves. All data were collected from the Good Doctor website, which is the largest OHC in China. We first used descriptive statistics to investigate all of the physician online contribution behavior. Then multiple linear and quadratic regression models were applied to verify the causal relationship between rewards and physician online contribution. Results: Our sample includes 40,300 physicians from 3,607 different hospitals, 10 different major specialty areas, and 31 different provinces or municipalities. The average increments of physician online contribution scores in Gynecology-Obstetrics and Pediatrics specialty areas were 413.5 and 362.2, respectively, much higher than others. Based on the multiple quadratic regression model, we found that the coefficients of the control variables, past physician online contributions, doctor review rating, clinic title, hospital level, and city level, were 0.415, 0.189, -0.099, -0.106, and -0.143, respectively. For the psychological (material) rewards, the standardized coefficient of the main effect was 0.261 (0.688) and the standardized coefficient of the quadratic effect was -0.015 (-0.049). All estimates were statistically significantly at a 0.1% level. Conclusions: Our study discovered that the averages of physician online contribution were different in ten specialty areas. Physicians with more past physician online contribution, with higher review ratings, with lower clinic levels, not coming from tertiary hospitals, and not coming from big cities, were more willing to participate in OHC activities. To promote physician online contribution, it is necessary to establish an appropriate incentive mechanism including psychological and material rewards. Finally, our findings suggest two guidelines for designing a useful incentive mechanism to facilitate physician online contribution. First, material reward is more useful than psychological...

show abstract

Section: Principal Resultssupporting

confidence: 82%

“…POC t is the past physician online contribution at time t [15,34]. DR t is the mean of doctor review ratings at time t [5,26].…”

Section: Control Variablesmentioning

confidence: 99%

Understanding a Nonlinear Causal Relationship Between Rewards and Physicians� Contributions in Online Health Care Communities: Longitudinal Study (Preprint)

Wang¹,

Chiu²,

Yu³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The success of online communities depends largely on sustained participations and voluntary contributions from users [38]. Researchers have revealed factors related to user participation in online communities, such as open-source software development [39], Wikipedia [40], and Question & Answering communities [41,42]. …”

Section: Introductionmentioning

confidence: 99%

Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective

Wang¹,

Zhao²,

Street³

2017

J Med Internet Res

146

164

View full text Add to dashboard Cite

BackgroundOnline health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users’ participations and predict user churn for user retention efforts.ObjectiveThis study aimed to analyze OHC users’ Web-based interactions, reveal which types of social support activities are related to users’ participation, and predict whether and when a user will churn from the OHC.MethodsWe collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users’ continued participation. Using supervised machine learning methods, we developed a predictive model for user churn.ResultsUsers’ behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC.ConclusionsDetecting different types of social support activities via text mining contributes to better understanding and prediction of users’ participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies.

show abstract

“…However, the usefulness of accumulated positive and negative ratings largely depends on how much noise (i.e., ratings not based on quality) the overall ratings contain (Chen et al, 2011;Khansa, Ma, Liginlal, & Kim, 2015;Poston & Speier, 2005). For instance, TripAdvisor, Yelp, or Amazon ratings that are based on personnel, social, or cultural factors and not on the quality of the product or service limit the usefulness of those reviews, which is problematic for both the buyer (not resolving seller uncertainty) and the seller (not being properly rewarded for having a superior product or service).…”

Section: Discussionmentioning

confidence: 99%

Noise or Quality? Cross-Nested Hierarchical Effects of Culture on Online Ratings

Mattson¹

2017

CAIS

View full text Add to dashboard Cite

Abstract:Previous feedback system research in a variety of contexts has focused on the impact that ratings (as proxies for quality) have on a variety of social and economic outcomes with equivocal findings. These mixed findings may be partially due to noise (factors not related to quality) embedded in aggregated or average positive and negative ratings. One significant source of ratings noise may come from culturally diverse raters' issuing ratings in virtual environments. Culture impacts how groups of individuals are socialized to behave and think, which may result in members' having different attitudes towards publicly downgrading (negative ratings) or praising (positive ratings) other members in the feedback system. In this paper, I investigate how culture influences rating practices specifically in public electronic knowledge sharing communities. Using a cross-nested hierarchical linear model, I demonstrate empirically that cultural differences at the community, occupation, and national levels interact in unique ways to increase or decrease an individual's propensity to give and receive a positive or a negative rating. My study contributes to the literature on rating systems along with having practical ramifications for the designers of feedback systems.

show abstract

Understanding Members’ Active Participation in Online Question-and-Answer Communities: A Theory and Empirical Analysis

Cited by 130 publications

References 91 publications

Understanding a Nonlinear Causal Relationship Between Rewards and Physicians� Contributions in Online Health Care Communities: Longitudinal Study (Preprint)

Understanding a Nonlinear Causal Relationship Between Rewards and Physicians� Contributions in Online Health Care Communities: Longitudinal Study (Preprint)

Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective

Noise or Quality? Cross-Nested Hierarchical Effects of Culture on Online Ratings

Contact Info

Product

Resources

About