By Steve Tracey📧 and Kusumal Ruamsook📧
In Supply Chain Management Review, November, 2023.
A major shift in the focus of branding has occurred in the last two decades. Traditionally, brands were associated with products, and consumers purchasing the products were often unaware of the company behind them. Today, a growing number of companies place greater emphasis on corporate brands and corporate-dominant brand architecture as a means to navigate the competitive and hyperconnected market environment. Boosted by the e-commerce boom, supply chain management has risen to the forefront of retail branding strategies. This article discusses how distribution and logistics are revolutionizing corporate branding strategies in the retail sector, going beyond the enabler of brand promises delivery to be an extension of the retailer brand.
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A major shift in the focus of branding has occurred in the last two decades. Traditionally, brands were associated with products, and consumers purchasing the products were often unaware of the company behind them. Today, a growing number of companies place greater emphasis on corporate brands and corporate-dominant brand architecture as a means to navigate the competitive and hyperconnected market environment. 
Customers are increasingly utilizing location-based services via mobile devices to engage with retail establishments. The focus of this paper is to identify factors that help to drive venue popularity revealed by location-based services, which then better facilitate companies’ operational decisions, such as procurement and staff scheduling. Using data collected from Foursquare and Yelp, we build, evaluate, and compare a wide variety of machine learning methods including deep learning models with varying characteristics and degrees of sophistication. First, we find that support vector regression is the best performing model compared to other complex predictive algorithms. Second, we apply SHAP (Shapley Additive exPlanations) to quantify the contribution from each business feature at both the global and local levels. The global interpretability results show that customer loyalty, the agglomeration effect, and the word-of-mouth effect are the top three drivers of venue popularity. Furthermore, the local interpretability analysis reveals that the contributions of business features vary, both quantitatively and directionally. Our findings are robust with respect to different popularity measures, training and testing periods, and prediction horizons. These findings extend our knowledge of location-based services by demonstrating their potential to play a prominent role in attracting consumer engagement and boosting venue popularity. Managers can make better operational decisions such as procurement and staff scheduling based on these more accurate venue popularity prediction methods. Furthermore, this study also highlights the importance of model interpretability which enhances the ability of managers to more effectively utilize machine learning models for effective decision-making.
Problem definition: In high-product-variety businesses like fashion, mass production (MP) systems create environmental waste in the form of overproduction on a colossal scale. Mass customization (MC) has been proposed—without solid evidence—as a solution. In this paper, we analyze whether MC 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 MC, charging a disposal fee for overproduction, and recycling. Academic/practical relevance: There is increasing interest in mass-customizing fashion goods, not only because consumers value customization, but also because MC is perceived to be environmentally friendly. Our paper puts this advocacy for MC to the test. We contribute to the literature, which has been largely silent on the issue, by uncovering when MC offers a win-win and relating such market outcomes to policy ideas. Methodology: We develop an analytical model of an MP firm adopting MC (going hybrid). The firm’s profit-maximizing variety, price, and inventory decisions then form the basis of our understanding the environmental impact of adopting MC and assessing various policy options. Results: Adopting MC can be a win-win, but it can also increase overproduction and hurt the environment. Our policy analyses reveal two kinds of insights. The first kind is about whether a policy expands win-win outcomes—encouraging sustainable adoption of MC. Among the policy ideas we explore, only promoting MC so as to increase consumers’ tolerance for waiting for mass-customized products can do that unambiguously. The second kind of insight is about whether a policy reduces the hybrid firm’s environmental impact. Only a disposal fee and costly recycling programs can do that unambiguously. Managerial implications: For MC adoption to be a win-win, policy makers must (1) work on convincing consumers to wait for bespoke fashion; (2) target MP firms with low cost of variety (high product-mix flexibility) with disposal fees or costly recycling programs; and (3) encourage those with relatively higher cost of variety to develop/acquire technology that would make recycling profitable.
We investigate analytical and empirical properties of the Heteroscedastic Exponomial Choice (HEC) model to lay the groundwork for its use in theoretical and empirical studies 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, which show that 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 30 product categories and find that it significantly improves model fit and generally improves out-of-sample prediction compared with 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.
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.
