How Patient Adopt Online Healthcare Information? An Empirical Study of Online Q&A Community
Hypothesis 3: The higher the sourcecredibility, the higher likelihood the message will be adopted
Hypothesis 2: The more the emotional support, the higher likelihood the message will be adopted.
According to elaboration likelihood model, people often use cues pertaining to the message’s source when they are unable or unwilling to expend the effort to elaborate on the message content (Petty et al. 1986). In online communities, the message is displayed in a threaded format.
Cues such as the popularity of the thread, the relationships among participants, participants’
profile (for example, reputation, interest, and skill), the interaction patterns and the evolution of the thread over time, and so on are visible on the interface. Those cues may effect as source credibility to measure the validity of message. We therefore test:
Hypothesis 3: The higher the sourcecredibility, the higher likelihood the message will be adopted.
Due to the openness of online community, any user can participate in the process of topic discussion. More than one solution is provided, and the recipient would choose the best answer for his/her question. In this paper, we regard members who participant in the topic discussion compete with each other to provide problem solutions. We assume that competition among the participants moderate the effect between adoption likelihood and its antecedents. Because of increased competition, customers would increase their demands with respect to information quality, emotional support and source credibility. We therefore test:
Hypothesis 4A: The importance of information quality on knowledge adoption will be stronger under conditions of higher competition among the participants.
Hypothesis 4B: The importance of emotional support on knowledge adoption will be stronger under conditions of higher competition among the participants.
Hypothesis 4C: The importance of source credibility on knowledge adoption will be stronger under conditions of higher competition among the participants.
Recipient expertise and recipient involvement are two of the most researched determinants of elaboration likelihood (Sussman et al. 2003). In online communities, it’s hard to observe
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recipient expertise, but we can follow recipient’s past experience recorded by the system to measure the degree of involvement, which include the involvement of the online community and the involvement of the discussing thread. Receivers that are highly involved with message issue are likely to engage in high elaboration, while those that are not involved will be less likely to engage in elaboration and more likely to be influenced by peripheral cues (Stamm et al. 1994).
We therefore test:
Hypothesis 5A: The importance of information quality on knowledge adoption will be stronger under conditions of higher involvement of recipient.
Hypothesis 5B: The importance of emotional support on knowledge adoption will be weaker under conditions of higher involvement of recipient.
Hypothesis 5C: The importance of source credibility on knowledge adoption will be weaker under conditions of higher involvement of recipient.
3 Empirical results and analysis 3.1 Research context and data corpus
This study took place at Baidu Know, which is the most popular online Q&A communility in China and where user puts a question and motivates other members to supply answers.
Healthcare module is one of the most popular module of Baidu Know, where questions about healthcare problems are proposed and members with related knowledges or similar experiences could provide answers for questions. The website records every detail of questions , answers, and members.The data corpus, which includes texts posted by members from March 2013, consists of 1722 threads.A thread is a collection of one question and several answers. There are two types of member in each thread – recipient and replier. The recipient is the member posting question and make adoption decision, and the replier is the member providing solutions.
3.2 Variable description
As stated in the second section, there are 5 latent variables, namely information quality, emotional support, source credibility, replier competition and recipient involvement. Based on data collected from Baidu Know, each variable will be measured as follow:
Information quality: The solution provided by the replier is the key content that recipient cared.
We will evaluate the information quality in the next aspects (Otterbacher 2009; Radev et al.
2004). With considering the relevance between the question and the replies, we apply (1) the cosine similarity between the reply and its question, (2) the centroid (textual centrality) score of the reply(Radev et al. 2004), (3) theoverlap between the reply and the question. Taking into account the linguistic characteristics of the replies, we apply (4) the ratio of object sentence to all sentences of a reply, (5) the average number of words in every sentence in the reply.Considering the amount of information in a reply, we apply, (6) the unique words in the reply.Considering the timeliness of reply, we apply (7) the time span between the reply and its question.
Emotional support: Semantic analysis was employed to abstract emotion element contained in the reply. First, we identify the subjective sentences of the messages (Ye et al. 2007). Two
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variables are employed to represent emotional support of reply, namely (8) the sentiment score of the reply, (9) the ratio of subject sentence to all sentences of a reply.
Source credibility:Baidu Know has an authoritative reputation system. Every member is assigned to certain level according to his contribution to the communities and help to other members. According to ELM, member’s reputation can be used to measure source credibility, and impact recipient’ adoption likelihood. In this paper, we apply (10) the reputation level of the replier, (11) the number of the adoption of the replier, and (12) the adoption ratio of the replier.
Replier competition: It is certain that recipient will be benefited from the fierce competition among repliers. In this paper, we apply three variables to evaluate the competition, including (13) the number of repliers, (14) the number of replies, the (15) average time span between consecutive replies and (16) the reputation level of the recipient.
Recipient involvement: There are two levels of involvement, first level of involvement is the extent to which recipient takes part in community activities, and the second is the extent to which recipient involves in the discussing thread. According to information provided by Baidu Know, we apply 4 variables to measure recipient involvement. The first 3 variables, which represent the first level of involvement, are (17) the reputation level of the recipient, (18) the adoption number of the recipient, (19) the adoption ratio of the recipient and number of questions proposed by the replier.The rest variable representing second level is (20) the number of sub-questions during the thread discussion process.
3.3 Hypothesis test
We test the hypothesized relationships among the constructs using binary regression model with the software program SPSS18.0. Hierarchical regression approach is employed to test the moderating effects of moderator variables (Angst et al. 2009; Baron et al. 1986). Table 1 presents the results of the binary logistics regression. As discussed, the dependent variable is a recipient’s decision to adopt a solution in a thread. The third column (Model 1) shows regression on all antecedent variables, namely information quality, emotional support and source credibility.
Model 2A and 2B illustrate the results with moderator variable replier competition. Model 3A and 3B presents the results with moderator variable recipient involvement.
Regression results are shown in third column (model1) in table 1, all of the three antecedent variables are significant correlated to the likelihood of adoption: the beta coefficient for information quality was 0.99 (p<0.01), while that of emotional support and source credibility is 0.44 (p<0.01) and 0.42 (p<0.01) respectively. Thus, hypothesis1, 2 and 3 are supported. Also, the coefficient of information quality is as twice big as that of emotional support and source credibility, indicating information quality is the most import factor for recipient’s adoption decision. Emotional support and source credibility have similar impact on adoption decision.
According to model 2A and 2B, only hypothesis 4C was supported (p<0.01), namely the importance of source credibility on knowledge adoption will be stronger under conditions of higher competition among the repliers. The results of model 3A and 3B indicate that moderating effects of recipient involvement between information quality and adoption likelihood, and source credibility and adoption likelihood are significant at the 0.1 level of significance. In contrast to
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Hypothesis 5C, the importance of source credibility on knowledge adoption will be stronger under conditions of higher involvement of recipient. So only hypothesis 5A was supported, namely the importance of information quality on knowledge adoption will be stronger under conditions of higher involvement of recipient.
Table 1Binary logistics regression results for adoption decision
Variable Model 1 Model 2A Model 2B Model 3A Model 3B
IQ – information quality; ES – emotional support; SC – source credibility; RC – replier competition; RI – recipient involvement.***p<0.01, ** p<0.05, *p<0.10.
4 Conclusion and Discussion
Although knowledge adoption has been widely researched, little attention was focued on healthcare knowledge adoption. Furthemore, most of the researches related to knowledge adoption employ questionare to collect emperical data. In this paper, we collected data from helathcare module of Chinese biggest online Q&A community — Baidu Know. Emperical result indicated that information quality, emotional support and souce credibility have positive and direct effect on adoption decision. Competition among repliers would moderate the relation between source credibility and adoption likelihood, namely the importance of source credibility on knowledge adoption will be stronger under conditions of higher competition among repliers.
Similarly, the extent of recipient involvement also has positive moderating effect between information quality and adoption likelihood, source credibility and adoption likelihood.
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