8/26/2023 0 Comments Download hotel with kaaba viewĭue to the exponential growth in the volume of data transmitted on social networking sites and the recognition of data as an economic asset, efforts are being made to actively utilize big social data in a variety of businesses (e.g., tourism and hospitality) and contexts (e.g., customer satisfaction and preference learning). According to previous research, 82% of US adults read online reviews and ratings before buying a new product or service. According to previous research, over 30% of internet users rate products online. ![]() The accessibility of travel websites facilitates customer feedback, and travelers are asked to provide both quantitative and qualitative reviews of hotels, destinations, and other travel experiences. Online customer reviews and ratings are the most accessible and prevalent form of electronic word of mouth. ![]() Customer feedback is commonly used by managers to identify future managerial objectives and to monitor a company's performance through customer satisfaction scores such as average customer satisfaction scores. It is possible to assess and respond to customer satisfaction levels as well as behavioral intentions using a variety of methods. In the tourism and hospitality context, it is critical for hotel managers to satisfy travelers and translate this satisfaction into behavioral commitment to remain competitive. Customer feedback is vital for business growth, customer experience improvement, and new product and service development. In the age of social media, consumers of products and services prefer to read feedback from other consumers before making a purchasing decision. In turn, it has been revealed that there is a link between attitudes and travel choices, with factors such as hotels’ performance criteria playing an important role. According to previous research, personal attitudes toward service are affected by customer satisfaction. Thus, strategic planning and decision-making are critical components of business success, and having a thorough understanding of travelers' preferences can assist hotel managers in these endeavors. The choice of hotel also serves as an example of a difficult decision-making process. Most international travelers place a high value on the quality of their accommodations. Traveler satisfaction, personalized service and a better hotel experience are among the top priorities for hotel managers, especially since good hotel service quality enhances travelers’ loyalty and influences their repurchase intentions. The results are discussed, and several recommendations and strategies for hotel managers are provided to enhance their service quality and improve customer satisfaction. The results show that the method is effective for big social data analysis and traveler segmentation in Mecca hotels. The data are segmented, and travelers’ satisfaction is revealed for each segment based on their online reviews of hotels. We collect data from travelers’ online reviews of Mecca hotels on TripAdvisor. To do so, we use support vector regression with sequential minimal optimization (SMO), latent Dirichlet allocation (LDA), and k-means approaches to develop the hybrid method. In this study, we aim to develop a hybrid methodology based on supervised learning, text mining, and segmentation machine learning approaches to analyze big social data on travelers’ decision-making regarding hotels in Mecca, Saudi Arabia. However, revealing customer satisfaction using big social data in the form of eWOM (electronic word of mouth) can be an effective way to better understand customers’ demands. ![]() There have been many studies on customer satisfaction that take quantitative survey approaches. Revealing customer satisfaction through the use of user-generated content has been a significant issue in business, especially in the tourism and hospitality context. Big social data and user-generated content have emerged as important sources of timely and rich knowledge to detect customers’ behavioral patterns.
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