The emergence of online health communities (OHCs) has enabled the use of information technologies to address some social and health needs including but not limited to emotional, social, and health-related issues. This information age has encouraged user generated (UG) content, which facilitates both peer-to-peer and business-to-peer interconnections. This rich and active information epoch (i.e., OHCs) is distinct in that value is generated when peers or participants—who may be content generators and/or content consumers—interact together by exchanging information and receiving supports aimed at addressing their specific needs; and this is made possible through the online platforms or support groups acting as the intermediary among users.
In this dissertation, I explore the dynamics that take place in OHCs by answering varied sets of questions and addressing and stretching different scholarly discourses including individuals’ information assessment, information disclosure, participation, and response behaviors in OHCs from a variety of theoretical perspectives including disclosure decision-making model and social presence theory, using diverse methodologies such as text analytics, two-stage least squares regression technique, decision trees analysis, and vector autoregression models in the OHC context. The overarching research question is: How does assessment of information and receiver influence patients’ disclosure ability and what user information disclosure mechanisms elicit effective support behaviors in online health communities? Patients with different disease types visit OHCs to get support and this support is made possible because patients participate by interacting with peers and providing responses to each other’s discussion. Support behaviors, especially in the OHC context, is a concept that covers facets such as, provision of response; interactivity or participation in discussions; relationship management; and offering helpful, appropriate, and relevant feedback responses to meet specific information, social, or emotional needs (Huang et al., 2019; Nambisan et al., 2016; Chen et al., 2019). By exploring the research question and with the unique features that these OHC platforms exhibit—the sharing of information, participation, and receiving of supports—these essays make the following contributions. Theoretically, the findings reveal that a patient’s disease type, the sensitivity of information being disclosed, and patient’s expectation of a response show unique effects on disclosure efficacy. These factors constitute mechanisms by which patients in OHCs are motivated to disclose health information in granular forms that elicit effective community responses and feedback. This information exchange mechanisms thereby, facilitate active community participation through giving or receiving of support, and thus, fostering a dynamic interplay between individuals’ disclosure and response behaviors in the online context. Practically, online health community managers can design their platforms to provide automated and customizable tools that improve patients’ information density and information breadth skills for effective response generation; and from the results, platform management can better understand users that are motivated to participate through giving, thereby encouraging those that are weak in receiving. Also, platform managers can improve the skills of those who are weak in giving for users that are motivated to participate through receiving.
Essay 1: Promoting Participants’ Information Disclosure and Response Behaviors in Online Health Communities: Disclosure Decision-Making Model Perspective
In this first essay, I extend the literature on information disclosure and the disclosure decision-making model (DD-MM) by examining the factors that influence information disclosure (disclosure efficacy) and the effects of disclosure efficacy on the response users receive (response efficacy) at the granular level. Until now, both concepts—disclosure efficacy and response efficacy have been conceptualized as single constructs. This current study breaks new grounds and broaden the DD-MM model by postulating that the subconstructs have different antecedents and consequences. By examining the relationships between the subconstructs of information assessment, disclosure efficacy, and response efficacy using the two-stage least squares regression method, the results reveal some insightful dynamics, otherwise not possible with unidimensional constructs.
Essay 2: Investigation of non-linear effects of first impression cues on participation in online health communities: A decision tree induction theory development approach
One notable phenomenon that prior literature has extensively explored in OHC platforms is user participation, which is a necessary condition for platform sustainment and value generation. Extant research has studied user participation as a form of giving, that is, how much users participate in online platforms by generating content (e.g., posting messages, replying to messages, or posting pictures).However, participation in OHC platforms can also take the form of receiving (the consumption for content that has been generated – e.g., reading other’s posts, gaining knowledge and support), and this has witnessed little attention in prior research. This third study argues that the giving and receiving participation is a reaction to user initial participation. In this second essay, based on social presence theory (SPT), I use decision tree analysis to interrogate the effect of first impression in the initial posts on users’ giving and receiving participation. The findings provide meaningful insights for advancing research and for assisting platform managers on what to focus on to encourage users’ giving or receiving participation on their platforms.
Essay 3: User Two-way Communication Efficacy Behaviors in Online Health Communities: A Longitudinal Study
In this second essay, I crack into some unsupported relationships between disclosure efficacy and response efficacy shown in the previous study, which could be due to the use of cross-sectional data in the analysis, giving nonsignificant findings. Over time, it is possible that the effectiveness of the response that disclosers receive could determine whether users will further disclose or not. For example, if a discloser does not receive valuable response that addresses his or her needs, he or she may stop posting or disclosing information on the platform, thus, leading to lurking behaviors or less recommendations for others to join the online platform. This current study proposes a two-way relationship between disclosure efficacy and response efficacy of users’ interactions in online health communities instead of looking at only the one-way relationship from disclosure efficacy to response efficacy (which showed some insignificant results). From an econometric perspective, time has been shown to play a dynamic role on variables and their relationships. Thus, this current paper uses dynamic vector autoregression (VAR) modeling technique with a longitudinal data set to investigate the one-way and two-way relationships between disclosure efficacy and response efficacy and their dimensions (information density and information breadth) and (information persuasiveness and response persuasiveness), respectively. The analysis reveals a recursive relationship between disclosure efficacy and response efficacy and some of their dimensions. This is a departure from some prior literature that proposed a static linear order in end-user information consumption. The significance of the nonlinear recursive relationship is marked extension of the DD-MM model by establishing the reenforcing effect of its key variables
