## 3 Empirical Study  
To construct an agent-based model more in line with the actual situation, we conducted an empirical study to assess the behavioral characteristics of AISs and PIRs in the multiple-stage microprocess of platform adoption driven by multiple promotional strategies. The empirical results can be considered part of the underlying assumptions of the agent-based model in the following section and the basis of the model parameter setting. In line with the previous literature [19; 39], AISs and PIRs can be in three states: state 1, that is, have never heard of the platform; state 2, that is, have heard of the platform but are not the users of this platform; state 3, that is, are already users of this platform. We designed questionnaires to further understand PIRs' and AISs' information sources (WOMs or mass media) from state 1 to state 2, their information processing process and decision-making methods from state 2 to state 3, the timing of platform users (state 3) to conduct WOM activities, their roles in WOM activities (opinion leaders or not), and how they become opinion leaders. We prepared 13 questions on multiple stages of platform adoption (see Table 2). Q1 and Q2 are designed to identify AISs and PIRs. The questions are designed according to the definitions of AISs and PIRs and are in line with previous literature [15; 50]. Q3 is developed based on the work of [PERSON] et al. (2011) [19] to identify the information sources of AISs and PIRs. The elaboration likelihood model proposed two routes of information processing [51]: central, which reflects the informational influences [52] corresponding to the personal judgments of the quality of the platform, and peripheral, which reflects the normal influences [53]. Therefore, Q4-Q6 are designed to assess the weight of informational influences and normal influences for AISs and PIRs when processing information. Q7 and Q8 are designed based on [PERSON] et al. (2000) [54] to identify the differences in the degree of compromise when interacting with others. The following questions are only for individuals who have joined the platform. Q9 is designed according to [PERSON] et al. (2015) [7] to find the timing when platform users are most likely to promote the platform. Previous literature divided platform users into opinion leaders and nonleaders according to their opinion influences [55]. Q10 is refined from the opinion leadership scale developed by [PERSON] (1971) [56]. We define the 10% who score highest on the scale as opinion leaders (OLs) [57; 58]. The other 90% of the platform users are nonleaders (NLs). Q11 are designed to assess the opinion influences of opinion leaders and nonleaders. Q12 is designed to assess the forwarding probability of the spread of WOM about the platform of opinion leaders and nonleaders. Q13 is designed to identify the necessary conditions to become an opinion leader for both AISs and PIRs. We invited three academic experts in the field of innovation diffusion and social platforms to evaluate the face and content validity of each item. The results of this phase indicated that most items performed well in representing their respective constructs. We employed 97 seniors as respondents who were users of a social platform on which Tai Chi teaching and organizing services are provided online and offline. Their relatives and friends outside the platform were also employed as respondents. We obtained 235 respondents (141 males, 94 females) between 50 and 75 years of age. Platform users were required to answer all the questions, but nonusers were required to answer only Q1-Q8 in Table 2, since the remaining questions were designed for platform users. We found that 41.7% of the respondents were AISs, while 58.3% were PIRs. According to the data, we confirmed some critical assumptions in the model, which was in line with previous literature. First, AISs can access more information sources than PIRs.