By A. Alptekinoglu📧, and Adem Orsdemir
Working Paper, 2020.
Problem definition: In high-product-variety businesses like fashion, mass production systems create environmental waste in the form of overproduction on a colossal scale. Mass customization has been proposed – without solid evidence – as a solution. In this paper, we analyze whether mass customization can indeed offer a win-win solution that helps both the bottom line and the environment. We also study the impact of three real policy options: promoting mass customization, charging a disposal fee for overproduction, and recycling. Academic / practical relevance: There is increasing interest in mass customization of fashion goods, not only because consumers value customization, but also because mass customization is perceived to be environmentally friendly. Our paper puts this advocacy for mass customization to test. We contribute to the literature, which has been largely silent on the issue, by uncovering when mass customization offers a win-win and relating such market outcomes to policy ideas. Methodology: We develop an analytical model of a mass producer firm adopting mass customization (going hybrid). The firm’s profit-maximizing variety, price and inventory decisions then form the basis of our understanding the environmental impact of adopting mass customization and assessing various policy options. Results: Adopting mass customization is a win-win in many scenarios, e.g., high (moderate-to-low) product value and moderate-to-high (moderate) product variety cost. Surprisingly, going hybrid can also increase overproduction and hurt the environment. Our policy analyses of the hybrid firm reveal that: promoting mass customization may not always help (in moderate- and low-value-product cases); charging a disposal fee for overproduction does always help; and recycling helps only if it is costly on a per unit basis (and not necessarily if profitable). Managerial implications: Mass customization can be a win-win, but it can also backfire on the environment. Policy interventions must be carefully thought through because some may have unintended consequences.
Keywords: Corporate social and environmental responsibility; Mass customization; Make-to-order; Mass production; Make-to-stock; Overproduction; Pricing; Product variety; Fashion industry
Access: Full paper here.
We investigate analytical and empirical properties of the Heteroscedastic Exponomial Choice (HEC) model to lay the groundwork for its use in theoretical and empirical research that build demand models on a discrete choice foundation. The HEC model generalizes the Exponomial Choice (EC) model by including choice-specific variances for the random components of utility (the error terms). We show that the HEC model inherits some of the properties found in the EC model: closed-form choice probabilities, demand elasticities and consumer surplus; optimal monopoly prices that are increasing with ideal utilities in a hockey-stick pattern; and unique equilibrium oligopoly prices that are easily computed using a series of single-variable equations. However, the HEC model has several key differences with the EC model that show variances matter: the choice probabilities (market shares) as well as equilibrium oligopoly prices are not necessarily increasing with ideal utilities; and the new model can include choices with deterministic utility or choices with zero probability. However, because the HEC model uses more parameters, it is harder to estimate. To justify its use, we apply HEC to grocery purchase data for thirty product categories and find that it significantly improves model fit and generally improves out-of-sample prediction compared to EC. We go on to investigate the more nuanced impact of the variance parameters on oligopoly pricing. We find that the individual and collective incentives differ in equilibrium: Firms individually want lower error variability for their own product, but collectively prefer higher error variability for all products – including their own – because higher error variability softens the price competition.
Given the importance of online reviews, digital platforms regularly redesign review systems to enhance platform performance. A unique platform governance aspect of the Apple App Store review system is their resetting of apps’ overall ratings each time app developers release an update. This means that with each update apps have to rebuild their reputation from scratch. Using panel data from the Apple App Store, we analyze the effects of developers’ updating behavior and of the rating resetting on subsequent app performance. Using an instrumental variable approach, we distinguish between feature and maintenance updates, and whether the app update is the developer’s choice or driven by the platform. We find strong indications for divergent effects on app performance subject to the type of update and its source. This suggest that developers can use updating as a strategic instrument and that platform governance has an impact on the strategic importance of this instrument.