Abstract
This chapter operates on objectives 4 and 5, for which four sections are presented in it. Sections 7.1 and 7.2 work for identification and labelling of consumer clusters. Cluster solution is validated in Sect. 7.3, and these segments are explained in Sect. 7.4 for what they symbolize. Overall, the division of Indian consumers is shown in three segments named: red, yellow, and green. Red is a segment of ‘apathetics and imprudents’, yellow segment comprises ‘aesthetics and hopefuls’, and green segment originates with ‘aspirants and illuminators’. In this way, green segment came out as a segment of ‘responsible consumers’, and the proportion of these consumers in Indian market came out to be approximately 47 percent.
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Notes
- 1.
Ward’s Method is employed as it is best suited with squared Euclidian Distance Measure (Hair et al. 2006: pp. 486–496).
- 2.
Hair et al. (2006: p. 506) suggests a reasonable approach to determine the number of clusters is to measure the percentage change in heterogeneity that is how different observations in one cluster are from another cluster.
- 3.
The significance of mean differences is tested using one-way ANOVA, and validity of cluster solution is determined using discriminant analysis. Facca and Allen (2011) suggested testing validity of cluster solution with discriminant analysis to have the truly classified cases and the percentage of correct classification.
- 4.
The strength of association (ω2) between dependent clusters and independent variables is calculated manually using the formula [SSB − (K − 1)MSW] ÷ [SST + MSW]. The abbreviations in the formula are visible in Table 7.4 and SST stands for ‘total sum of squares’ (SSB + SSW).
- 5.
The effect sizes in ANOVA have been interpreted and defined by Laroche et al. (2002: p. 274). They mentioned that a coefficient of 0.01 can be interpreted as a low effect size, 0.06 as a medium effect size, and if any coefficient exceeds 0.15 it is termed as large effect size.
- 6.
The correlation matrix in discriminant analysis is called as pooled within-group correlation matrix as it involves the average correlation for the two or more correlation matrices for each variable pair (George and Mallery 2006: p. 279).
- 7.
In the words of Churchill et al. (2010: p. 498), correlation coefficient between any pair of predictors that exceeds 0.80 is considered to be the existence of multicollinearity in that pair of variables. Here the degree of correlations is not exceedingly high from this value of 0.80 for multicollinearity to be a problem.
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Gupta, K., Singh, N. (2020). Segmentation of Consumers and Identification of Responsibles. In: Consumption Behaviour and Social Responsibility. Approaches to Global Sustainability, Markets, and Governance. Springer, Singapore. https://doi.org/10.1007/978-981-15-3005-0_7
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