سعیده ممتازی

Nowadays, electronic word-of-mouth (eWOM) has become the primary source of tourism-related information. Travelers are increasingly seeking additional information provided in eWOM platforms to minimize the complexity and insecurity involved in making a purchase decision. However, there still is a lack of research on the impact of eWOM on travelers’ green hotel booking intentions. Therefore, this study aims to develop a theoretical model to examine the effects of positive and negative eWOM on travelers’ green hotel booking intentions and provide practical guidelines for hotel marketing and policymaking. To do this, a model was developed based on the theory of planned behavior (TPB) while positive and negative eWOM were linked to the model as two new factors. This study utilized a quantitative research approach and data collection was performed through an online survey questionnaire. Data was collected from 418 travelers who had the experience of searching on social media for collecting travel-related information. The statistical software SmartPLS and SPSS were used to analyze the data. Findings showed that customer attitudes, subjective norms, perceived behavioral controls, and positive eWOM positively influenced travelers’ green hotel booking intentions. In contrast, the influence of negative eWOM on travelers’ green hotel booking intentions was not supported. The findings of this study can assist hotel managers in social and content media policymaking. Meanwhile, they can help policymakers in the tourism industry develop optimum policies. This study can provide new capabilities for policymakers to address existing challenges and opportunities and promote green practices in the hotel sector.

We present FarsWikiKG, a Persian knowledge graph extracted from Wikipedia. Wikipedia infoboxes have been used as a valuable resource for building knowledge graphs in recent years. FarsWikiKG consists of more than 2 million entities, as well as 5.7 million facts about the entities. Using Wikidata, we constructed an ontology with more than 6000 classes representing entity types. As the second Persian knowledge graph, which has the ability of self-update, FarsWikiKG shows improvement on NLP tasks, especially question answering systems. Although FarsWikiKG is a dynamic knowledge graph, our evaluation shows a coverage of 90% on Persian Wikipedia pages. As Wikipedia information is constantly changing, a fixed knowledge graph can provide unstable data to the user. The proposed system, in addition to solving the problem of unstable data, reduces the need for experts to extract and construct knowledge graphs manually. Storing information in RDF as a standard method of storing knowledge graph information, FarsWikiKG allows NLP systems to run SPARQL queries on it.