## 2 Literature Review and Theoretical Framework
### Market Segmentation in Education  
Market segmentation refers to categorising a population into distinct and internally homogenous sub-groups based on shared characteristics, including socio-demographics, attitudes, or behavioural traits [57; 58]. For segmentation to be effective, the identified groups must reflect high quantifiability, stability, and internal similarity [57; 59; 60]. There are two primary segmentation approaches. A priori (common-sense) segmentation is based on predefined criteria (i.e., interest group or socio-demographic variables), where the segments are determined based on prior knowledge. This approach is regarded as more straightforward due to its lack of methodological limitations [61]. In contrast, a posteriori approach is utilised when no predefined group exists, thus requiring data-driven techniques (i.e., factor analysis or cluster analysis) to uncover naturally occurring segments [57; 62]. For this study, the first phase entailed selecting maritime engineering students from the Faculty of Maritime Studies at the University of Rijeka, Croatia, following the common-sense approach. In a subsequent phase, a data-driven approach was adopted by rating the student experiences with the perceived immersion of the HMD-VR engine room simulator.  
Market segmentation has been used as an effective management and decision-making strategic tool in different domains, including tourism and leisure [63; 64], e-commerce and retailing [65], banking [66], sports industry [67], and medicine and healthcare [68]. Given the increasing competitiveness among higher education institutions in recent decades, market segmentation studies have become essential for positioning themselves effectively in the educational market [69]. To date, a variety of segmentation bases have been employed to distinguish the heterogeneity of the student population, including rational/emotional factors [70], perceived service quality expectations and requirements [71; 72], attitudes toward social media [73], behavioural traits [74], and socio-demographic [75].  
While most of the abovementioned studies have primarily been oriented toward student recruitment, this paper draws attention by employing a technology-driven approach to segmentation, using perceived immersion--one of the key subjective experiences provided by VR technology--as the foundation for student classification. Despite continuous investment in VR technologies across various educational domains [20], including maritime education and training [19], there remains a limited understanding of the potential of affective variables, such as perceived immersion, as a reliable foundation for profiling students' attitudes toward learning outputs and future intentions of using VR technology. To the best of the authors' knowledge, the only existing study that has explored the heterogeneity of student population in the context of VR-based maritime education is [32], which employed the Technology Acceptance Model (TAM) component perceived ease of use as the basis for classification. By extending this research to perceived immersion, this study aims to provide new insights into the potential of using affective variables as the classification criteria to distinguish student attitudes toward learning benefits, engagement, and adoption of HMD-VR technology in maritime higher education.