Journal ArticleDOI
Examining the role of various psychographic characteristics in apparel store selection: a study on Indian youth
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TLDR
In this paper, the authors identify the role of psychographic characteristics in apparel store selection among Indian youth of a tier-II city and identify four psychographic clusters: Get Going Adopters, Disinterested Introverts, Confused Followers and Independent Life Lovers.Abstract:
Purpose – The current study aims to identify the role of psychographic characteristics in apparel store selection among Indian youth of a tier‐II city.Design/methodology/approach – The research instrument was based on focus group discussions and in‐depth interviews of young people and retailers. It consisted of a psychographic scale and a store scale. Data were collected from young students aged between 19 and 24 years.Findings – Cluster analysis found four psychographic clusters: “Get Going Adopters”, “Disinterested Introverts”, “Confused Followers” and “Independent Life Lovers”, and the differences between these segments were found to be statistically significant. The findings suggest that “Independent Life Lovers’ consider apparel shopping to be a recreational activity, whereas “Get Going Adopters” prefer to spend less time in stores; both these segments were driven by layout, ambience and the availability of the latest designs and styles in apparel store selection. “Confused Followers”, who struggle t...read more
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Journal ArticleDOI
Examining the role of consumer lifestyles on ecological behavior among young Indian consumers
TL;DR: In this article, the authors identify what lifestyles best explain and impact ecological behavior among young Indian consumers, and adapts the lifestyle scale developed by He et al. (2010) and the Actual Commitment scale to the Indian context to describe its impact on the young consumer's ecological profile.
Journal ArticleDOI
A structural model of the antecedents and consequences of Generation Y luxury fashion goods purchase decisions
TL;DR: In this paper, an integrative theoretical model is proposed based on social comparison theory, social impact theory, the perceived quality model and theory of uniqueness to predict the antecedents and consequences of Generation Y luxury fashion goods purchase decisions.
Journal ArticleDOI
Store format choice and relationship quality in apparel retail: A study of young and early-middle aged shoppers in New Delhi region
Gaurav Tripathi,Kartik Dave +1 more
TL;DR: In this article, the authors compared three different retail formats (Discount stores, Exclusive stores and Multi-brand outlets) based on the shopper's perception of relationship quality (RQ) of these stores using multivariate techniques.
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Behavioural Aspects Influencing Decision to Purchase Apparels amongst Young Indian Consumers
TL;DR: The study of consumer buying behavior is essential for marketers to understand the key elements such as what is purchased from where it is purchased, the quantity of purchase, how much mo... as mentioned in this paper.
Book ChapterDOI
Challenges and Opportunities of E-Tailing in Emerging Economies
Ritu Narang,Prashant Trivedi +1 more
TL;DR: In this paper, the authors have discussed the opportunities, challenges and unique issues associated with e-tailing in emerging markets and concluded with a discussion on the future of e-tailing in emerging nations.
References
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Diffusion of Innovations
TL;DR: A history of diffusion research can be found in this paper, where the authors present a glossary of developments in the field of Diffusion research and discuss the consequences of these developments.
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Multivariate Data Analysis
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
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Multivariate data analysis
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.