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.

As developing economies become more industrialized, the energy problem has become a major challenge in the twenty-first century. Countries around the world have been developing renewable energy to meet the Sustainable Development Goals (SDGs) of the United Nations (UN) and the 26th UN Climate Change Conference of the Parties (COP26). Leaders of enterprises have been made aware of the need to protect the environment and have been practicing environmental marketing strategies and green information systems (GISs) as part of ESG practices. With the rapid growth of the available data from renewable electricity suppliers, the analyses of multi-attribute characteristics across different fields of studies use data mining to obtain viable rule induction and achieve adaptive management. Rough set theory is an appropriate method for multi-attribute classification and rule induction. Nevertheless, past studies for Big Data analytics have tended to focus on incremental algorithms for dynamic databases. This study entails rough set theory from the perspective of the decrement decay alternative rule-extraction algorithm (DAREA) to explore rule induction and present case evidence with managerial implications for the emerging renewable energy industry. This study innovates rough set research to handle data deletion in a Big Data system and promotes renewable energy with valued managerial implications.