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This article examines the development of discriminant functions which will best discriminate between the urban and rural consumers with respect to the social factors influencing the consumer behavior in the two-wheeler market in India. The paper further highlights the importance of various promotional tools in the purchase of two wheelers in India. The two-wheeler market in India is the largest segment of the domestic auto industry in terms of absolute numbers. From a semi luxury product for the urban middle class in the eighties and earlier, the two-wheeler has now become not only the favorite mode of personal transport but also the most coveted personal household durable possession among nearly all consumer classes, except perhaps the most affluent. The leading vehicle in the two-wheeler segment is the stylish, fuel-efficient and sturdy 4-stroke motorcycle, the purchase of which is influenced by various social factors. A descriptive research using stratified random sampling method was undertaken and the study was conducted using both primary and secondary data. It was concluded that the “social status” factor was the most effective social factor in discriminating among the urban consumers and ‘primary reference group’ factor was the most effective social factor in discriminating among the rural consumers in the choice of a two-wheeler brand.
Dr. Byloppilly. 2018. "A Discriminant Analysis of the Social Factors Influencing the Consumer Behavior in the Two-wheeler Market in India- An Empirical Study". Global Journal of Management and Business Research, Global Journal of Management and Business Research - E: Marketing GJMBR-E Volume 18 (GJMBR Volume 18 Issue E6).
Crossref Journal DOI 10.17406/GJMBR
Print ISSN 0975-5853
e-ISSN 2249-4588
v1.2
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Total Score: 173
Country: United Arab Emirates
Subject: Global Journal of Management and Business Research
Authors: Dr. Rameshwaran Byloppilly (PhD/Dr. count: 1)
View Count (all-time): 274
Total Views (Real + Logic): 1206
Total Downloads (simulated): 56
Publish Date: 2018 01, Mon
Monthly Totals (Real + Logic):
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