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Halal certification is an acknowledgment of the halalness of a product or service issued by a halal regulator based on Islamic law. This study aims to investigate the intentions of consumers and regulators toward blockchain-based halal certification. Blockchain is useful for storing and verifying halal certificates, thereby increasing trust in products or services because the public cannot change or access data once it is stored.

This study uses a triangulation approach by distributing online questionnaires to consumers as a research instrument of a quantitative approach processed with smart partial least squares. Meanwhile, the qualitative approach is carried out through observation, in-depth interviews with the Ministry of Religion’s Halal Product Assurance Organizing Agency (BPJPH) and Halal Examination Agency (LPH), and forum group discussions (FGDs) with several related parties.

The observation results show that most consumers expect the government to provide an easy-to-use application to check halal food products and restaurants. Consumers’ intention to use this technology is influenced directly by attitudes and indirectly by their beliefs. Furthermore, the results of interviews and FGDs reported that LPH was not ready to apply blockchain technology, while BPJPH strongly supported adopting blockchain technology in the certification process.

This finding recommends that the Indonesian government apply blockchain technology to gain transparency and accountability regarding the halal product process.

This study fills the research gap by observing three perspectives from different stakeholders and using a triangulation approach to analyze the need for adoption of blockchain-based halal certification of halal food products.

This study aims to examine the tourists' visit intention by watching deepfake destination videos, using Information Manipulation and Media Richness Theory.

This study conducted a primary survey utilizing a structured questionnaire. In total, 1,360 tourists were surveyed, and quantitative data analysis was done using PLS-SEM.

The results indicate that the factors that affect the tourists' visit intention after watching deepfake videos include information manipulation tactics, trust and media richness. This study also found that perceived deception and cognitive load do not influence the tourists' visit intention.

The originality/salience of this study lies in the fact that this is possibly among the first to combine the Media Richness Theory and Information Manipulation for understanding tourists' visit intention and post-viewing deepfake destination videos.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.