Investigating the Influence of Empowerment on Patients’ Satisfaction: How to Empower Patients in Online Health Consultation Platform

Online health consultation platform becomes a significant channel for health consumers to seek online support and health consultation. As the health consultation moves from offline to online, it significantly changes the communication circumstance between patients and physicians. It is crucial to understand the empowerment process embedded in online physician-patient interaction, in turn to improve patients’ satisfaction on line health services. This study examined how social-structural empowerment and psychological empowerment affect patients’ satisfaction, as an empowerment outcome, in the online health consultation platform using text mining techniques and econometric analysis. Our results indicate that informational and emotional support can extrinsically empower patients and thereby increase their satisfaction. Psychological empowerment is also found that has two roles in the empowerment process, a partial mediating effect and a moderating effect on the relationship between social-structural empowerment and patients’ satisfaction. This study enriches the empowerment theory from a text mining perspective and extends the empowerment theory in the organizational context to the context of digital health

Detecting Blurred Boundary Advertisement in Social Media Marketing Platform

Consumers seek organic reviews of products and services (organic eWOM) to verify whether they made the right choice (Erkan and Evans, 2016). However, the authenticity of brand promotion (e.g., blurred boundary advertisement) hinders consumers’ decision making. Consumers are calling for transparency of information between social platforms and users. This study is to develop efficient models to distinguish blurred boundary advertisements from organic eWOM. Drawing upon dual-process theory, we developed logistic models which can distinguish blurred boundary advertisements from organic eWOM in social media marketing platforms with decent explanations. Blurred boundary advertisement can be detected by features about posts, comments, bloggers and followers. Moreover, number of followers, number of posts and number of comments showed U- shape relationships with detecting blurred boundary advertisement. With more accurate statistical and machine learning-based models, this study helps consumers and platforms solve possible fairness issues in digital marketing

Modeling forest fire occurrences using count-data mixed models in Qiannan autonomous prefecture of Guizhou province in China.

Forest fires can cause catastrophic damage on natural resources. In the meantime, it can also bring serious economic and social impacts. Meteorological factors play a critical role in establishing conditions favorable for a forest fire. Effective prediction of forest fire occurrences could prevent or minimize losses. This paper uses count data models to analyze fire occurrence data which is likely to be dispersed and frequently contain an excess of zero counts (no fire occurrence). Such data have commonly been analyzed using count data models such as a Poisson model, negative binomial model (NB), zero-inflated models, and hurdle models. Data we used in this paper is collected from Qiannan autonomous prefecture of Guizhou province in China. Using the fire occurrence data from January to April (spring fire season) for the years 1996 through 2007, we introduced random effects to the count data models. In this study, the results indicated that the prediction achieved through NB model provided a more compelling and credible inferential basis for fitting actual forest fire occurrence, and mixed-effects model performed better than corresponding fixed-effects model in forest fire forecasting. Besides, among all meteorological factors, we found that relative humidity and wind speed is highly correlated with fire occurrence

Leveraging Collective Social Capital and Team Diversity to Promote Online Medical Team Performance

Online medical team service is a relatively new type of service offered by online healthcare consultation (OHC) platforms. This study explores the impacts of offline and online collective social capital on medical team performance. Furthermore, we examine three types of team diversity (i.e., separation, variety, and disparity) and investigate the moderating effect of team diversity on the relationship between social capital and team performance. We collected data from the most popular OHC platform in China, and 785 teams with 2750 physicians are included in the data set. The empirical findings indicate that: (1) offline and online collective social capital positively influence medical team performance; (2) separation strengthens the impact of offline collective social capital while weakens the impact of online collective social capital; (3) variety weakens the impact of offline collective social capital while strengthens the impact of online collective social capital. Key findings, implications, and limitations are also discussed

Changes in Doctor–Patient Relationships in China during COVID-19: A Text Mining Analysis

Doctor–patient relationships (DPRs) in China have been straining. With the emergence of the COVID-19 pandemic, the relationships and interactions between patients and doctors are changing. This study investigated how patients’ attitudes toward physicians changed during the pandemic and what factors were associated with these changes, leading to insights for improving management in the healthcare sector. This paper collected 58,600 comments regarding Chinese doctors from three regions from the online health platform Good Doctors Online (haodf.com, accessed on 13 October 2022). These comments were analyzed using text mining techniques, such as sentiment and word frequency analyses. The results showed improvements in DPRs after the pandemic, and the degree of improvement was related to the extent to which a location was affected. The findings also suggest that administrative services in the healthcare sector need further improvement. Based on these results, we summarize relevant recommendations at the end of this paper

Histogram of forest fire occurrence data in the spring fire season from January to April between 1996 and 2007 in Qiannan autonomous prefecture of Guizhou province, China.

<p>Histogram of forest fire occurrence data in the spring fire season from January to April between 1996 and 2007 in Qiannan autonomous prefecture of Guizhou province, China.</p

Location of Qiannan autonomous prefecture in Guizhou province, China.

<p>Location of Qiannan autonomous prefecture in Guizhou province, China.</p

Meteorological variables during the spring fire season in Qiannan autonomous prefecture from 1996–2007.

<p>Meteorological variables during the spring fire season in Qiannan autonomous prefecture from 1996–2007.</p

Diagnostic plots for the Poisson mixture fixed-effects models and mixed-effects models. d_j is the difference between the predicted probability and the observed probability, as shown in Equation (7).

<p>Diagnostic plots for the Poisson mixture fixed-effects models and mixed-effects models. d_j is the difference between the predicted probability and the observed probability, as shown in Equation (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120621#pone.0120621.e007" target="_blank">7</a>).</p

Parameter estimations and fit statistics for twelve models.

<p>Note: ** significant at 0.05 level,</p><p>* significant at 0.1 level.</p><p>Parameter estimations and fit statistics for twelve models.</p