In the context of growing sustainability awareness on social media, this research investigates the comparative effectiveness of brand official accounts versus mega-influencer accounts in green advertising. Drawing on the Elaboration Likelihood Model (ELM), six primary studies, including one field study and five experiments, reveal that brand accounts are more effective in fostering favorable consumer attitudes toward green products. Brand accounts, perceived as credible and authoritative, are more likely to elicit central route processing, enabling deeper engagement with complex and technical information about products’ green attributes. In contrast, mega-influencer accounts primarily trigger peripheral route processing, relying on heuristic cues like charisma and popularity, which are less effective in conveying detailed sustainable claims. The research identifies elaboration and message understandability as sequential mediators, and shows that contextual factors—such as response timing, counter-persuasion, and psychological distance to environmental issues—moderate the relative effectiveness of account types. This research advances the theoretical understanding by clarifying the distinct roles of social media account types in green marketing and expanding the application of ELM in this domain. The findings offer practical implications not only for marketers, but also for policy-makers and consumer advocacy groups seeking in promote effective and credible environmental messaging on social media.

Radio frequency identification (RFID) allows automated identification of products by embedding chips with wireless antennas on objects. RFID uses transponder technology for the contactless recognit...

Abstract In this paper, a new method for fuzzy clustering is proposed that combines generative topographic mapping (GTM) and Fuzzy c‐means (FCM) clustering. GTM is used to generate latent variables and their posterior probabilities. These two provide the distribution of the input data in the latent space. FCM determines the seeds of clusters, as well as the resultant clusters and the corresponding membership functions of the input data, based on the latent variables obtained from GTM. Experiments are conducted to compare the results obtained using FCM and the Gustafson‐Kessel (GK) algorithm with the proposed method in terms of four cluster‐validity indexes. Using simulated and benchmark data sets, it is observed that the hybrid method (GTMFCM) performs better than FCM and GK algorithms in terms of these indexes. It is also found that the superiority of GTMFCM over FCM and GK algorithms becomes more pronounced with the increase in the dimensionality of the input data set.