Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions.

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs.

The dataset for validation of customer inspiration construct in Malaysian context.

This study intended to validate customer inspiration (CI)in Malaysian/developing country context. Data were collected from two different respondents for two studies - from Millennial customers of the auto industry and Generation Z customers of the smartphone industry. The survey conducted through a standardized and structured questionnaire.

Survey dataset on Muslim's religiosity, Muslim personality and work behavior.

Data were collected from administration officers ranging from middle-management to top management of the five universities of Malaysia. The data was collected through a standardized and structured questionnaire. The variables of the study were religiosity, personality and work behavior of Muslims.