مطالب

The present study tries to analyze the role of emotional intelligence of service providers in the trip process (before, during, and after) with a different view of sustainable tourism (from the perspective of tourist satisfaction) and emphasis on the human spiritual needs, to pave the way for further access tourist satisfaction and implementation of sustainable tourism. This is an applied exploratory research and has been done by content analysis and system thinking (to draw a conceptual model qualitatively and validated by experts). Data was collected through in-depth interviews with experts in sustainable tourism and spirituality. Experts were recruited through purposeful snowball sampling until data saturation and theoretical saturation. based on the findings, service providers' emotional intelligence plays an important role in improving the travel process by (A) helping the tourist in choosing the right travel package (before the trip), (B) creating a sense of calm, cheerfulness, and happiness in the tourist through responding to his spiritual needs (during the trip) and (C) impact positively in the mental evaluations and future decisions of tourist (after the trip). Based on systemic thinking, the results showed that paying attention to the tourists' spiritual needs through the employees' emotional intelligence is effective in achieving more sustainable tourism, so according to research findings and the widespread role of emotional intelligence in the travel process, training of service providers in the use of emotional intelligence is recommended to activists and planners in this industry

The number of traveling Muslims has increased substantially, and demands for Islamic facilities and services that adhere to their religious practices are growing exponentially. This creates business opportunities for service providers in Muslim and non-Muslim countries alike. Propelled by the growth of this new market segment, service providers such as airlines and hotels have customized aspects of their businesses to accommodate these niche demands. However, some industry players willfully ignore this market, either for fear of losing non-Muslim customers or lacking the know-how to tap into this lucrative new market. This research benchmarked some service providers (hotels) worldwide and conducted a content analysis of their websites to identify the services they provided for Muslims. The evidence shows that some hotels worldwide provide limited services to satisfy Muslim needs. Most of these hotels did not provide information about their Islamic facilities on their website, including some hotels in Islamic countries. This can be related to the social pressure (non-Muslim tourist markets), which prevents service providers from advertising their services. Lack of knowledge and research on the factors that prevent service providers from serving Muslim tourists and little information about their challenges make this area unexplored. Future research should test the research model proposed by this study and identify the factors influencing the service provider’s intention to serve the Muslim market and the barriers. This paper can serve as a guideline for industry players to partake in this growing and lucrative market.

The majority of wireless sensor network (WSN) security protocols state that a direct connection from an attacker can give them total control of a sensor node. A high level of security is necessary for the acceptance and adoption of sensor networks in a variety of applications. In order to clarify this issue, the current study focuses on identifying abnormalities in nodes and cluster heads as well as developing a method to identify new cluster heads and find anomalies in cluster heads and nodes. We simulated our suggested method using MATLAB tools and the Database of the Intel Research Laboratory. The purpose of the performed simulation is to identify the faulty sensor. Using the IBRL database, sensors that fail over time and their failure model is the form that shows the beats in the form of pulses, we find out that the sensor is broken and is of no value. Of course, this does not mean that the sensor is invasive or intrusive. We have tried by clustering through Euclidean distance that identify disturbing sensors. But in this part of the simulation, we didn't have any data that shows disturbing sensors, it only shows broken sensors. We have placed the sensors randomly in a 50 x 50 space and we want to identify the abnormal node.