Introduction
Problem Statement
Service quality can be defined as the discrepancy between a customer’s perception and expectation of service (Namin, 2017). In order to gain a competitive advantage, businesses continually seek innovative ways to provide high-quality services and differentiate themselves. With increasing global competition in the hotel industry, businesses aim to expand their markets and raise customer expectations through technological advancements and globalization. Consequently, it becomes imperative for firms to prioritize customer satisfaction, which is significantly influenced by service quality (Uzir et al., 2021).
Practical Rationale
Service quality plays a pivotal role in predicting customer satisfaction, making it crucial for the hotel industry to focus on delivering exceptional service that aligns with customers’ perceived quality elements (Lu et al., 2015). This emphasis on service quality from the customer’s perspective is a vital determinant of performance in the tourism and hotel sectors. Researchers and professionals in various service industries, including tourism and hotels, have shown great interest in measuring service quality and evaluating its impact on consumer satisfaction.
Theoretical Rationale
The relationship between service quality and customer satisfaction has been extensively explored in theoretical and empirical research across multiple industries, such as online banking, retail, and services. Several frameworks, including LODGSERV, LQI, SERVQUAL, and HOLSERV, have been developed to assess hotel, lodging, and hospitality service quality (Minh et al., 2015). Numerous studies have utilized these scales to evaluate customers’ perceptions of hotel service quality. This study specifically focuses on the five dimensions of SERVQUAL, namely tangibility, responsiveness, dependability, assurance, and empathy, which assess service quality before and after consumption.
Research Method
Sample
Convenience sampling was employed to select an appropriate sample for this research. Although convenience sampling is a commonly used non-probabilistic sampling method, its application should be cautiously approached. In this study, convenience sampling involved selecting respondents conveniently available to the researcher, such as students who frequently visit restaurants. Three respondents were selected based on convenience by asking students who had dined at restaurants about their satisfaction with the service quality. There was no intentional pattern in the selection process.
Data Collection Technique
In this study, interviews were conducted to gather information from the participants. Interviews are a qualitative research technique that involves posing questions to collect data. The interviewer interacts with two or more individuals, seeking to understand their experiences, perceptions, opinions, or motivations. To ensure a successful research interview, the interviewer must establish a trusting rapport with the interviewee(s). Three interview formats, structured, semi-structured, and unstructured, are commonly used in qualitative research (Adhabi & Anozie, 2017). Structured interviews follow a predetermined list of questions, enhancing data validity and reliability but potentially limiting interviewee responses. Unstructured interviews lack predetermined questions but have a planned list of topics to cover, allowing greater flexibility at the expense of data reliability (Wilson, 2013). Semi-structured interviews balance structure and flexibility, making them a popular choice. For this study, semi-structured interviews were employed, allowing the interviewer to ask prepared questions and explore topics in various ways with different interviewees, thus enhancing adaptability, response quality, and data dependability.
Analytical Technique
Data analysis is a crucial step in conducting credible qualitative research. The qualitative researcher’s ability to comprehend, articulate, and interpret experiences and perceptions is vital in elucidating meaning within specific situations and contexts (Thorne, 2016). Consequently, the qualitative researcher is often referred to as the research instrument. In this study, a thematic analysis was chosen to understand the gathered data. This approach involves identifying patterns or themes in qualitative data and is considered the fundamental technique for learning in qualitative research (Vaismoradi et al., 2013). Notably, this method offers the advantage of being a technique rather than a rigid methodology, making it conducive to learning and teaching (Talan et al., 2020). Unlike many qualitative techniques, thematic analysis is not bound by a specific theoretical or epistemological stance, providing flexibility that is particularly beneficial in educational settings. Given the variety of learning and teaching activities, this flexibility proves advantageous. The primary purpose of thematic analysis is to identify significant or intriguing patterns, referred to as themes, in the data and subsequently utilize these themes to discuss the study or support key points. According to Braun & Clarke (2022), it is important to note that a robust thematic analysis goes beyond mere data summarization; it involves comprehensive explanation and clarification. A common mistake is to use the primary interview questions as themes, which often indicates a failure to analyze the data adequately (Kiger & Varpio, 2020).
Measures of reliability
Consistency plays a crucial role in ensuring the reliability of qualitative research. By maintaining a consistent approach and epistemological framework, researchers can generate comparable data regarding their ontological nature, even though they may vary in richness and contextual nuances within similar dimensions (Ozuem et al., 2022). While qualitative research allows for a certain degree of variability in outcomes, enhancing the dependability of the research process and findings is important. Five methods were suggested to improve the dependability of the qualitative research: refutational analysis, continual data comparison, extensive data utilization, the inclusion of aberrant instances, and the use of tables. Researchers should perform refutational analysis, constantly comparing and contrasting the data collected from various sources. This process, known as triangulation, helps ensure the accuracy of the data in terms of its form and context (Abdalla et al., 2018). Additionally, researchers should strive to make the scope and analysis of the data as comprehensive as possible, considering quantitative characteristics if feasible.
In summary, as Noble & Smith (2015) described, maintaining consistency in qualitative research is essential for establishing dependability. Employing methods such as refutational analysis, continual data comparison, extensive data utilization, including aberrant instances, and using tables can enhance the reliability of the research process and findings. Rose & Johnson (2020) added that researchers should also engage in triangulation to verify the accuracy of the data and strive for comprehensive analysis incorporating quantitative aspects when appropriate.
References
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