An empirical study on dynamics of decision making parameters of working women while buying apparel in the city of Ahmedabad

Neha Patel, Rajeshwari Jain


Since 2015, India has enjoyed the position of the fifth largest market for textile and apparel products in the world, preceded only by developed nations like USA and countries in the European Union. In India, women apparels constitute 35% of the overall apparel market valued at 19 billion USD and is expected to outpace the men’s wear and occupy 41% of the market share by 2025 according to PWC(2018). Apparels play a very important role in the lives of individuals and especially women. It is a means of enhancing one’s personality and it boosts an individual’s confidence levels. With an increasing number of women entering the workforce in India, the apparel industry is expected to see phenomenal growth. Indian women are beginning to get more control over personal and household financial decision making - a huge cultural shift that is gaining significance for retailers to understand the working women’s buying behaviour for apparels in terms of fashion, comfort, style, social pressures among others. In this concept, consumer decision-making style inventory (CSI) of Splores and Kendell (1986) has been a useful tool for deciding on the decision making parameters by working women when it comes to buying apparels for themselves. This study proposes to determine the dynamics of decision making parameters in apparel products of working women in the city of Ahmedabad. A questionnaire based survey method which included the scale of Consumer Style Inventory (CSI) with 25-items was adapted. 150 responses across 25 items were collected and documented for analysis. Results of analyses showed that working women could be clustered through consumer decision-making styles as impulsive buyers, store loyal, hedonist fashion conscious, opportunist and priority of work attire.

Keywords: Female consumer decision-making parameters, female apparel, working women, Ahmedabad, Factor analysis decision clusters.

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ISSN : 2251-1571