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Establishing predictive relationships between price and select product, market, and consumer related dimensions: an investigation within indian consumer electronics market

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  • Mohit Goswami

    (Operations Management Group, Indian Institute of Management Raipur)

Abstract

This research intends to empirically examine within the ambit of Indian consumer electronics market the relationship between product prices and select product, market, and consumer related facets. Product distinctiveness (function of product commonality), consumer choice probabilities and numeral ratings of consumer reviews associated with product offerings within a market segment are considered the representative elements of product, market and consumer related dimensions respectively in this research. Initially, employing the one-way Analysis of Variance (ANOVA) technique, the test for statistical equality of product offering’s (of key market players) prices within a market segment is carried out. Thereafter, employing the multi linear regression (MLR) technique, the empirical relationships between product pricing and product distinctiveness, consumer choice, and consumer ratings are established. The devised research framework is demonstrated for the smartphone product offerings listed at the website of India’s leading e-retailer (Flipkart) in the price band of Rs. 5000–10,000. Our analysis reveals several key managerial insights. An important insight that results from our study pertains to the fact that consumer ratings provided by end users in this market segment does not seem to influence the product pricing considerably. Another noteworthy insight refers to the fact that subject to certain threshold price within this segment, the consumers really does not discriminate in purchasing product offering(s) of one manufacturer from the other.

Suggested Citation

  • Mohit Goswami, 2018. "Establishing predictive relationships between price and select product, market, and consumer related dimensions: an investigation within indian consumer electronics market," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 361-380, June.
  • Handle: RePEc:spr:opsear:v:55:y:2018:i:2:d:10.1007_s12597-017-0327-4
    DOI: 10.1007/s12597-017-0327-4
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    References listed on IDEAS

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    1. Mohit Goswami & M. K. Tiwari, 2015. "Product feature and functionality driven integrated framework for product commercialization in presence of qualitative consumer reviews," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 4769-4788, August.
    2. Xuemei Zhang & Yao Wei & Jiqiong Liu & Gang Chen, 2015. "Product Design Strategy with Commonality by Considering Customer-Choice Behavior in Supply Chain," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(05), pages 1-22.
    3. Gautam Gowrisankaran & Marc Rysman, 2012. "Dynamics of Consumer Demand for New Durable Goods," Journal of Political Economy, University of Chicago Press, vol. 120(6), pages 1173-1219.
    4. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    5. Chorus, Caspar & van Cranenburgh, Sander & Dekker, Thijs, 2014. "Random regret minimization for consumer choice modeling: Assessment of empirical evidence," Journal of Business Research, Elsevier, vol. 67(11), pages 2428-2436.
    6. Kostyra, Daniel S. & Reiner, Jochen & Natter, Martin & Klapper, Daniel, 2016. "Decomposing the effects of online customer reviews on brand, price, and product attributes," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 11-26.
    7. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    8. Kajal Chatterjee & Samarjit Kar, 2016. "Multi-criteria analysis of supply chain risk management using interval valued fuzzy TOPSIS," OPSEARCH, Springer;Operational Research Society of India, vol. 53(3), pages 474-499, September.
    9. Suzanne Fogel & Dan Lovallo & Carmina Caringal, 2004. "Loss Aversion for Quality in Consumer Choice," Australian Journal of Management, Australian School of Business, vol. 29(1), pages 45-63, June.
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