Premium
A Conjoint‐Hazard Model of the Timing of Buyers' Upgrading to Improved Versions of High‐Technology Products *
Author(s) -
Kim SangHoon,
Srinivasan V.
Publication year - 2009
Publication title -
journal of product innovation management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.646
H-Index - 144
eISSN - 1540-5885
pISSN - 0737-6782
DOI - 10.1111/j.1540-5885.2009.00658.x
Subject(s) - upgrade , conjoint analysis , product (mathematics) , computer science , hazard , new product development , econometrics , marketing , operations research , economics , microeconomics , business , mathematics , chemistry , geometry , organic chemistry , preference , operating system
Understanding consumers' upgrading behavior is essential to product planning. Product managers would like to know what fraction of customers would upgrade to new and improved versions, and how fast. This paper presents a method to forecast the sales path of an improved version of a high‐technology product defined in terms of its price path and multiattribute product specification. The approach is potentially useful to managers to answer what‐if questions on the effects of alternative price paths and product specifications of the upgrade on when and what fraction of customers will upgrade. By doing such analysis for several product options under consideration, managers can choose the best feature specification and price path for the upgrade. The proposed approach integrates an individual‐level conjoint utility model with a hazard function specification. The first stage of estimation (i.e., conjoint analysis) measures individual‐level multiattribute utility functions, and the second stage (i.e., duration analysis) calibrates the coefficients of predictor variables of the time to upgrade via maximum likelihood. An illustrative application in the personal digital assistant (PDA) category confirms the predictive validity and potential usefulness of the proposed approach. Among the empirical findings are that higher upgrade costs and expectation of faster product improvement tend to delay buyers' upgrading decisions. The roles of other predictor variables such as product category characteristics, consumer characteristics, and peer pressure were also confirmed.