Purpose: Up to 89.1% of consumers have changed their decisions due to the influence of online reviews. In order to increase the number of reviews, some websites and platforms have adopted feedback incentives. Research shows that offering review incentives does increase the likelihood of consumers writing reviews. However, the review reward system also brings some problems. Consumers may doubt the authenticity of product reviews that are incentivized by rewards, believing that reviewers are only writing reviews for personal gain, which has a negative impact on the credibility of the reviews. To address this issue, this study proposes a mechanism for mandatory disclosure of review incentives as research demonstrates that transparent online communication of a platform’s efforts can substantially boost review helpfulness restore trust in online platforms, and even improve purchase intentions. Design/methodology/approach: This study applies the S-O-R (Stimulus-Organism-Response) theoretical framework and utilizes a simulated situational questionnaire to investigate the impact of online reviews on consumers' emotional and behavioral responses within the context of a mandatory disclosure review reward system. The model identifies four key constructs: review quality as the Stimulus, review credibility and review usefulness as the Organism, and purchase intention as the Response. Data were collected through the questionnaire and subsequently analyzed to understand these relationships. Findings: The results show that review quality has a significant positive impact on review credibility, review usefulness and purchase intention. Similarly, review credibility and review usefulness also have significant positive impacts on purchase intention. Originality/value: To mitigate the potential negative effects of review reward systems, this study introduces a mandatory disclosure of the review reward and examines its impact using a simulated situational questionnaire. The study hypothesizes that mandatory disclosure of rewarded reviews enables consumers to identify these reviews, which may, in turn, enhance their purchase intentions.

In recent years, with the development of Web2.0, enterprises, government agencies, and traditional news media, which have been positively influenced by opinion leaders, have been dedicated to understanding leaders’ opinions on the web in order to seek convergence. Specifically, with the increase of environmental awareness, the introduction of green energy and carbon reduction technology has become an important issue. Consequently, studies identifying opinion leaders and followers who are interested in green energy and low carbon have become important. This study aims to find a solution that can identify the characteristics of opinion leaders and followers that can be widely used, which will help certain public policies or issues to be more effectively disseminated in the future. To model the characteristics of opinion leaders and their influence on followers, this study uses a dual matrix. The interaction patterns are recognized among opinion leaders and followers, with the aim of developing public policy to promote green energy and low carbon emissions. A case is studied to validate the superiority of the proposed solution approach. With the proposed approach, a (business) organization can identify and access opinion leaders and their followers. Through communication, these organizations can absorb strain and preserve functions despite the presence of adversity. This study also clearly demonstrates its contribution and novelty through comparisons with the existing alternative method.

Globally, 20% to 40% of medical resources are wasted, which could be avoided through professional audit of health insurance claims. The professional audit can pinpoint excessive use of unnecessary medicines and medical examinations. Taiwan’s National Health Insurance Bureau (TNHIB) deducts the weight that medical resources carry if regarded as unnecessary or abused when examining health insurance claims. The ratio of the deducted weight to the total weight claimed by a hospital is defined as the health insurance claim deduction rate (HICDR). A high HICDR increases the operating expenses of the hospital. In addition, it takes the hospital many resources to prepare and file appeals for the deduction. This study aims to: (1) minimize the weight deducted by the TNHIB for a hospital; and (2) facilitate efficient appeals to claim denials. It is expected that HICDR will be reduced through big data analytics. In this study, evidence-based medicine (EBM) is involved to clarify the debate, dilemmas, conflicts of interests in examining health insurance claims. A natural language method—latent Dirichlet allocation (LDA), was used to analyze patients’ medical records. The topics derived from the LDA are used as factors in the logistic regression model to estimate the probability of each claim to be deducted. The experimental results on various medical departments show that the proposed predictive model can produce accurate results, and lead to more than 41.7% reduction to the deduction of the health insurance claims. It is equivalent to more than a 750 thousand NT dollars saving per year. The efficiency of application is validated compared to the manual process that is time-consuming and labor intensive. Moreover, it is expected that this study will supplement the insufficiency of traditional methods and propose a new and effective solution to reduce the deduction rate.