Wine purchasing decisions in India from a consumer’s perspective: An analysis of influencing factors on the buying behavior


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This paper analyzed the factors affecting consumers’ purchase intentions and decisions while purchasing wine. The study was performed based on the primary data collected with the help of a survey and a structured questionnaire using convenience sampling. A total of 120 respondents above 21 years old across India who were wine consumers and likely to purchase wine were the study population. Exploratory factor analysis, confirmatory factor analysis, descriptive analysis, and cluster analysis were the main methods used to analyze the data. The information gathered was subjected to further quantitative analysis using SPSS. Kaiser-Meyer-Olkin Measure (KMO) of sampling adequacy was 0.759, and Cronbach’s alpha was 0.817 indicating high reliability of the study. Factor analysis brought out six essential factors affecting the wine purchase decisions of Indian consumers. They are as follows: quality concerns, consumption preferences, consumption deterrents, consumption reasons, social factors, and risk factors. Furthermore, the study found that the purchase intentions of Indian wine consumers are affected by the attitude and awareness of consumers. The cluster analysis further helped to divide the Indian wine market into three segments, i.e., regular consumers comprising 44.2%, non-consumers comprising 29.2%, and occasional consumers comprising 26.7%. A few of the key factors influencing wine purchase are attributes and knowledge of the wine ingredients. In addition, friends and family play an important role in wine purchasing decisions.

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    • Figure 1. Conceptual framework of different factors and sub-factors
    • Figure 2. Model of hypotheses and variables
    • Figure 3. Confirmatory factor analysis
    • Table 1. Demographic profiling of wine consumers
    • Table 2. Variables, factors, and reliability
    • Table 3. Outcome of confirmatory factor analysis
    • Table 4. Relation among the variables
    • Table 5. Percent of cases in each cluster
    • Table 6. Final cluster centers
    • Table 7. Cross-tabulation of demographic variables and the clusters
    • Conceptualization
      Anagha Ravikumar, Dipen Paul, Dharmesh K. Mishra
    • Data curation
      Anagha Ravikumar
    • Formal Analysis
      Anagha Ravikumar, Dharmesh K. Mishra
    • Methodology
      Anagha Ravikumar, Sushant Malik, Asmita Chitnis, Dharmesh K. Mishra
    • Validation
      Anagha Ravikumar, Asmita Chitnis, Dipen Paul, Dharmesh K. Mishra
    • Writing – original draft
      Anagha Ravikumar, Sushant Malik
    • Writing – review & editing
      Anagha Ravikumar, Sushant Malik, Asmita Chitnis, Dipen Paul, Dharmesh K. Mishra
    • Software
      Sushant Malik, Dipen Paul
    • Supervision
      Sushant Malik, Asmita Chitnis, Dharmesh K. Mishra
    • Investigation
      Asmita Chitnis, Dipen Paul, Dharmesh K. Mishra