Modelling lifestyle-based segments of Indian wine consumers using the latent profile analysis approach

Purpose>This study aims to identify and validate the different clusters of wine consumers in India based on the wine-related lifestyle (WRL) instrument. It also investigates how the identified clusters differ in terms of socio-demographic characteristics, such as age, gender, income, education, employment and marital status.Design/methodology/approach>The authors conducted a survey using a structured questionnaire to collect data from wine consumers in India. The number of participants totalled to 432. The authors first identified the clusters using latent profile analysis. The authors then used the decision tree analysis based on a recursive partitioning algorithm to validate the clusters. Finally, the authors analysed the relationship between the identified clusters and socio-demographic characteristics using correspondence analysis.Findings>Three distinct segments emerged after data were subjected to latent profile analysis, namely, curious, ritualistic and casual. The authors found that the curious cluster had a high mean score for situational and social consumption while the ritualistic cluster had a high mean for ritualistic consumption. The findings also suggest that the casual cluster had more female wine consumers.Originality/value>This study makes methodological contributions to the wine consumer segmentation approach. First, it adopts a latent profile analysis to profile Indian wine consumers. Second, it validates the obtained clusters using the decision tree analysis method. Third, it analyses the relationship between the identified clusters and socio-demographic variables using correspondence analysis, a technique far superior to the Chi-square methods.

Determinants of hospitality students' perceived learning during COVID 19 pandemic: Role of interactions and self-efficacy.

Our study adopts the Theory of Transactional Distance (TTD) as the theoretical framework to investigate the impact of the four interaction levels: content, instructors, peers, and technology on perceived learning among hospitality students with self-efficacy as the moderating factor. The data sample for the study includes responses from 461 hospitality students from various institutes in India. Our findings reveal that all the four-point of interactions, content, instructors, peers, and technology, have a significant positive impact on perceived learning. Further, learners' interaction with the content was emerged as the most significant predictor of perceived learning. The data was put to moderation analysis, with results suggesting that self-efficacy has a conditional effect only on the interaction between content and perceived learning.