Abstract
ABSTRACT Past research on product upgrades has focused either on understanding who and when will upgrade or on figuring out why consumers will upgrade, but seldom on all. It has also neglected the interplay between these matters with decision context and timing. This manuscript depicts a comprehensive approach where, for the first time, product characteristics, individual differences, process, and contextual variables are analyzed on a predictive model of real product upgrades, identified through the systematic collection of primary data from a panel of smartphone consumers. We tested one traditional linear logistic regression model and two types of non-linear, state-of-the-art machine-learning models (extreme gradient boosting and deep learning) to explain upgrading behavior. Results provide an integrative, yet parsimonious, product-upgrade model showing the importance of resources; news about the smartphone brand; sentimental value; predicted, current, and remembered enjoyment; update capacity; and how much the smartphone meets the user’s current needs as the most relevant variables to determine which consumers are more prone to upgrade their smartphones. Our findings advance upgrade decision theory by taking a holistic approach to the phenomenon and bridging different theoretical accounts of the replacement decision literature.
Highlights
Every year, like clockwork, companies line up at their conferences, trade shows, through press releases to announce their latest and greatest
Given the unknowns of how, why, and when individuals upgrade their current products and the lack of studies tackling this phenomenon in its entirety, we propose and test a comprehensive, integrative model of product upgrade, including personal differences, product characteristics, context, and psychological processes
Throughout this discussion, we will take into account both the contribution of each variable, manifested through their F1 scores, and their correlation to the upgrade decision
Summary
Like clockwork, companies line up at their conferences, trade shows, through press releases to announce their latest and greatest. The present work combines factors related to the ownership of the status quo product, the perception about the status quo, context variables, individual traits, demographic characteristics, enjoyment with the status quo, and desire for the upgrade to develop a model that explains the decision to upgrade (see Figure 1). It enhances prior understanding by considering multiple constructs and showing which variables are more relevant to predict which consumers will replace. These reasons make up the foundation of a research environment that is richer and more insightful than that of the launch of other widespread durables (e.g., refrigerators and televisions)
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