Key attributes of Muslim-friendly hotels’ service quality: voices from booking.com

Abstract

Purpose On a global scale, the Muslim-friendly hospitality business has intensified hotel competition. Given the paucity of research on the important service quality characteristics of Muslim-friendly hotels, this study aims to identify the major themes encountered by tourists at Muslim-friendly hotels. Design/methodology/approach The research used content analyzes (qualitative) to examine 1,250 reviews using Leximancer software. Data were gathered from the online travel website booking.com. The top 10 Islamic hotels according to Crescent ranking were taken into a data set. Findings Qualitative (narratives) analysis showcased nine key themes, namely, “hotel,” “staff,” “food,” “room,” “location,” “pool,” “facilities,” “cleanliness” and “Wi-Fi.” Furthermore, the findings of this study contribute to filling research voids in the literature by distinguishing themes linked with halal hotel “satisfaction” from those associated with “dissatisfaction.” Originality/value The findings of this research offer valuable visions into halal-hotel travelers’ overall experiences based on user-generated content and facilitate the identification of the dominant themes linked with a different value for money ratings.

How about the service perception during the COVID-19 pandemic: an analysis of tourist experiences from user-generated content on TripAdvisor

“…For example, Kim et al (2016) found that staff and their "attitude," "room cleanliness/dirtiness," "bed," "bathroom" and "room size" were identified as common satisfiers and dissatisfiers. While the work of Alrawadieh and Dincer (2019) study found that the most often hotel e-complaints included service quality, hotel facility efficiency, and cleanliness and hygiene, Arasli et al (2021) found that guests who score their hotel experiences as high satisfaction share narratives such as cleanliness, food, staff, hotel, restaurants, room and entertainment. Similarly, Arasli et al (2020b) researched cruise tourism.…”

“…Because of the widespread use of Web 2.0 technology in a wide range of fields, Web-based platforms may generate user-generated content (UGC) (Brochado et al , 2019). In addition, third-party agents, such as online reviews on Booking.com and Expedia (Arasli et al , 2021) and specific review portals such as Yelp and TripAdvisor, can generate UGC (Sulu et al , 2022). Consumers and decision-makers have benefited much from UGC analysis (Nilashi et al , 2021).…”

Air-Travelers’ Perceptions of Service Quality during the COVID-19 Pandemic: Evidence from Tripadvisor Sites

“…Through a three-part procedure [75], this type of map enables analysts to acquire insights into and comprehension of natural language (Figure 1). The software extracted semantic pattern information that enabled us to identify the key concepts and themes underlying passenger airline experiences in a concept map composed of concepts-represented by grey dots-that are then grouped into themes-represented by colored spheres [76]. The theme colors signify the significance of each theme, with themes heat-mapped from hottest to coolest (i.e., red-orange is the "hottest" or most noticeable theme, and purple is the "coolest" or least connected theme).…”

“…Tseng et al [4] collected data from Chinese travel blogs to research destination images. Another recent study done by Arasli et al [76] found key attributes of Muslim-friendly hotels' service quality using Leximancer. Brochado [79] researched nature-based experiences in tree houses focusing on guests' online reviews.…”

“…An elevated level of reliability can help mitigate researcher bias and increase the repeatability of results, but inconsistencies in how coders classify material can undermine the reproducibility of coding results [82]. Attaining a high degree of reliability in human-coded content analysis is typically difficult and much more so when processing large amounts of data since this increases the likelihood of the researcher making an error in the phase of coding the themes [76]. Leximancer software was utilized to analyze the data.…”