How often should a firm modify its products? A Bayesian analysis of automobile modification cycles

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• 作者：Goksel Yalcinkaya ; Tevfik Aktekin ; Sengun Yeniyurt ; Setiadi Umar
• 关键词：Product modification ; Bayesian inference ; MCMC estimation ; Finite mixtures ; Generalized gamma models ; New product development
• 刊名：Marketing Letters
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：28
• 期：1
• 页码：85-97
• 全文大小：
• 刊物类别：Business and Economics
• 刊物主题：Marketing;
• 出版者：Springer US
• ISSN：1573-059X
• 卷排序：28

文摘

In this paper, we develop and estimate a series of Bayesian generalized gamma family and mixture family models of product modification timing that take into account both firm- and industry-specific effects that are time varying. Our models are capable of capturing non-standard modification behavior such as multi-modality and non-monotonicity in the hazard rates motivated by real data. Additionally, we explore the existence of latent groups with respect to product modification timing strategies. The models are estimated using Markov chain Monte Carlo (MCMC) methods using a panel data from the automotive industry that covers 50 years and contains 6598 car model-year observations for 683 car models. The results reveal the non-monotonic modification behavior over time and the existence of three latent groups. Larger product portfolios and higher industry product proliferation lengthen the modification time. Brand and competitive modification dynamism increases the frequency of major modifications.