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. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

In the recent years, the rapid growth of the tourism industry has risen to prominence as a global concern. Tourism empowers communities and uplifts the economy. However, it poses social and environmental challenges, which in turn draws attention to tourism patterns. Sustainable tourism promises protection of the environment and the social-cultural elements of any given destination. Hence, this study aims to understand the complex relationship between sustainability policy, management, and tourist behavior. Thus, we examined the relationships between sustainable tourism policy and destination management, destination social responsibility, and tourist value orientation with sustainable tourism development. We recruited participants at managerial level coming from 163 Malaysian companies and conducted a cross-sectional quantitative study, using partial least square structural equation modeling. We propose that sustainable tourism policy and destination management and destination social responsibility significantly impact sustainable tourism development. Moreover, destination social responsibility partially mediates the relationship between sustainable tourism policy destination management and sustainable tourism development.

Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

The global use of Web 2.0 applications has generated enormous volumes of user content. Drawing on cognitive load theory, this study examines unexplored factors that influence gift purchase intention of tourists. The authors identify localization and realtime information for shaping tourists' gift purchase intention, which is facilitated by reduced cognitive overload. Analyzes of the study relies on a sample of 273 foreign tourists in Malaysia. A cross-sectional quantitative study is conducted using partial least square structural equation modeling. Results showed that location-based user-generated content and real-time information significantly affect gift purchase intention of tourists. Moreover, real-time information partially mediates the relationship between location-based user-generated content and gift purchase intention.

The paper seeks to investigate key factors influencing the personal development of tourists. This study examines the relationship between participatory consumer-generated content and tourists’ capabilities, emotions, and skills, as well as the modera...

Contextual suggestion systems have been emerging as an entrancing region of research, attributable to the innovative advances in smart connecting things and rapid growth of Big Data. In this regard, the primary purpose of contextual suggestion systems is to propose things that assist users to settle on choices from countless activities, for example, according to their specific context, system may predict that what place users would find interesting to visit or on what restaurant they would prefer to eat. In a smart environment using big data, users’ current activity and past behavior could be incorporated into the suggestion process with an end goal is to provide right suggestion at the right time with appropriate location on users personal preferences. The objective of this paper is to provide an overview of contextual suggestion system and a review of TREC’s contextual suggestion track to investigate the approaches have been used in order to develop a model for contextual suggestion.