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Gender-based differences in consumer decision-making styles: implications for marketers

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Abstract

Gender-based segmentation is frequently used by marketers to cater to different needs and preferences of men and women. However, the literature in consumer behaviour has not explored enough the role of gender in decision-making. Specifically, there are hardly any studies that investigate the decision-making styles of Indian male and female consumers. Based on gender and sociocultural theories, this study develops and tests hypotheses on gender-based differences in decision-making styles, including hedonism, novelty consciousness, price value consciousness and brand loyalty orientation. Analysis of data obtained from 355 male and 203 female students through self-administered questionnaire indicates that women score higher than men on hedonism, novelty orientation and price value consciousness. No significant difference was found on the dimension of brand loyalty. The study contributes to the consumer behaviour research by highlighting the differences in decision-making styles of Indian millennial men and women. The results can help marketers to employ gender-based segmentation and target more effectively. The study provides insights that firms can use in framing product, pricing and communication strategies catering to the requirements of a specific gender.

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Acknowledgements

The author is grateful to the editor and the anonymous reviewers for their valuable feedback, which significantly improved the presentation of this paper.

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  1. Marketing Group, Indian Institute of Management Calcutta, Kolkata, 700104, India

    Ritu Mehta

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Appendices

Appendix 1

Principal components analysis (PCA)

PCA uses dependencies between the variables to extract a small number of factors from a larger number of interrelated variables. These factors termed as principal components are orthogonal linear combinations of the measured variables that maximize the total variance. The first factor is constructed such that it explains the largest portion of the total variance, the second that accounts for most of the residual variance and so on. For better interpretation of the extracted factors, we used varimax rotation wherein the original solution is rotated so as to get new factors that correlate well (near − 1 or 1) with some variables and close to zero with others. These correlations between the extracted factor and the variables termed as factor loadings help with factor interpretability.

Appendix 2

Cronbach’s alpha

The coefficient, Cronbach’s alpha, is used as a measure of reliability of a scale. The coefficient measures internal consistency that is how closely related are the different items of a scale. The value of the coefficient ranges from 0 to 1 with high values normally indicating that the items measure the same construct and the scale is internally consistent. The coefficient alpha is calculated as the average of all possible split-half reliability coefficients. In split-half reliability, the items on the scale are divided into two halves and the resulting half scores are used to get correlation coefficient. Cronbach’s alpha value of 0.6 or lower generally indicates unsatisfactory internal consistency reliability.

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Mehta, R. Gender-based differences in consumer decision-making styles: implications for marketers. Decision 47, 319–329 (2020). https://doi.org/10.1007/s40622-020-00252-8

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