Recently, the Ministry of Health Pakistan decreed that disclosures of anti- smoking messages placement in the movies should be used whereby fear based cognitive anti-smoking message is shown. Consistent with the Elaboration Likelihood Model (ELM) assumption that persuasion messages likely influence the attitude of the relatively unmotivated individuals. This study is the first which tests and compare the ability of anti-smoking persuasion messages presented in the Direct Anti-smoking public service Advertisements (DAA) and Anti-smoking messages Placement Disclosures in movies (APD) in determining viewer’s Attitude to Quit Smoking (AQS). Hence, this attempt overcomes deficient understanding about the effects of disclosing anti-smoking advertisement placement. In a between-subject experiment (N = 120), we measure the smokers’ attitudes in the result of the direct anti-smoking advertisements along with the APDs in movies. Results of the experiment show that attitude to quit smoking becomes stronger when a direct anti-smoking advertisement and anti-smoking disclosure is shown. However, analyses based on Partial Least Squares Path Modeling (PLS-PM) on Advanced Analysis for Composites (ADANCO) 2.0.1 (a new software for variance-based SEM) attitude to quit smoking is significantly higher when the anti-smoking message disclosure is shown during the movie scenes. These results have significant implications for persuasion theories and public policy about anti-smoking campaigns.

Corporate climate disclosures have come to the fore of measuring environmental responsibility, but worries about greenwashing of exaggeration or parts of the environmental performance of exaggerating or overselling environmental performance remain. This paper fulfills this crucial gap in establishing the validity of such revelations by offering the machine learning method of identifying possible greenwashing. It is probable that the mixed-methods design has been used, where the textual analysis of the composed corporate sustainability reports and supervised learning algorithms trained on labeled examples of misleading statements are supplemented. Through the implementation of natural language processing and classification algorithms, the model will recognise patterns that are suggestive of a lack or even exaggeration of commitment with regard to climate pledges. The findings can be used to illustrate industry-related patterns and important language indications linked to greenwashing.

Generative AI is potentially efficient in the newsrooms, but raises concerns about the issue of credibility and trust. We evaluate its effect and the results of 600 articles each with a stratified content analysis of each production mode (human/AI-assisted/AI-generated) and with disclosure (none/minimal/rich) (1) to determine its effect on accuracy, sourcing, and correction latency; (2) a preregistered 3 × 3 experiment manipulating production mode and disclosure (none/minimal/rich) to determine its effect on perceived article credibility and brand trust. Higher error and hallucination rates and fewer named sources, and slower corrections of AI-generated items are demonstrated by content analysis. Minimal AI labels diminish credibility and trust experimentally, but rich, process-level disclosure, naming, editorial verification, and sources mitigate penalties of work assisted by AI. We give policy and legitimacy implications to the newsroom.

Generative AI tools are reshaping academic writing. The central issue is not their use, but when AI-assisted text can still be recognised as legitimate scholarly work. This exploratory study examines how experienced academics evaluate legitimacy through three conditions: retention of human responsibility for core ideas, limitation of AI to supportive roles, and disclosure of its use. Data were collected from 25 participants through a questionnaire combining rating scales and open-ended responses. The findings show that legitimacy is conditional rather than binary. Participants accepted AI for drafting, rephrasing, and organising text, but expressed concern when it shaped arguments or interpretations. Across responses, three conditions consistently defined acceptable use: AI must support rather than replace intellectual work, authors must remain accountable for all claims, and AI involvement must be disclosed. Legitimacy, therefore, rests on ongoing professional judgment rather than fixed rules.