This paper offers a new identification strategy for disentangling structural state dependence from unobserved heterogeneity in preferences. Our strategy exploits market environments where there is a choice-consumption mismatch. We first demonstrate the effectiveness of our identification strategy in obtaining unbiased state dependence estimates via Monte Carlo analysis and highlight its superiority relative to the extant choice-set variation based approach. In an empirical application that uses data of repeat transactions from the car rental industry, we find evidence of structural state dependence, but show that state dependence effects may be overstated without exploiting the choice-consumption mismatches that materialize through free upgrades.
This chapter focuses on how digitization can accelerate convergence in science and innovation, examining how it can support design, operation, and adaptive learning for the system as a whole and for actors along supply chains and markets. It briefly reviews key tenets of convergence science and innovation (CI). The first key enabler of CI is the combination of recent advances scientific understanding of real-world human behavior, with the knowledge the people have on intersectoral and multi-scale pathways creating real-world contexts. Behavioral analytics, artificial intelligence, and other digital technologies can inform the design of theory-informed and evidence-based health/food/nutrition-promoting innovation and/or interventions, be these of a digital, social, or physical nature. Multi-stakeholder digital platforms and research infrastructures to support agri-food innovation are currently emerging around the world. Convergence research and innovation may sketch a powerful distributed alternative to the still prevailing vertically integrated model of agri-food systems.
Learners often utilize online resources to supplement formalized curricula, and to appropriately support learning, these resources should be of high quality. Thus, the objectives of this study are to develop and provide validity evidence supporting an assessment tool designed to assess the quality of educational websites in Otolaryngology- Head & Neck Surgery (ORL-HNS), and identify those that could support effective web-based learning. METHODS: After a literature review, the Modified Education in Otolaryngology Website (MEOW) assessment tool was designed by a panel of experts based on a previously validated website assessment tool. A search strategy using a Google-based search engine was used subsequently to identify websites. Those that were free of charge and in English were included. Websites were coded for whether their content targeted medical students or residents. Using the MEOW assessment tool, two independent raters scored the websites. Inter-rater and intra-rater reliability were evaluated, and scores were compared to recommendations from a content expert.The MEOW assessment tool included a total of 20 items divided in 8 categories related to authorship, frequency of revision, content accuracy, interactivity, visual presentation, navigability, speed and recommended hyperlinks. A total of 43 out of 334 websites identified by the search met inclusion criteria. The scores generated by our tool appeared to differentiate higher quality websites from lower quality ones: websites that the expert "would recommend" scored 38.4 (out of 56; CI [34.4-42.4]) and "would not recommend" 27.0 (CI [23.2-30.9]). Inter-rater and intra-rater intraclass correlation coefficient were greater than 0.7.Using the MEOW assessment tool, high quality ORL-HNS educational websites were identified.
Typical recommender systems try to mimic the past behaviors of users to make future recommendations. For example, in food recommendations, they tend to recommend the foods the user prefers. While the recommended foods may be easily accepted by the user, it cannot improve the user's dietary habits for a specific goal such as weight control. In this paper, we build a food recommendation system that can be used on the web or in a mobile app to help users meet their goals on body weight, while also taking into account their health information (BMI) and the nutrition information of foods (calories). Instead of applying dietary guidelines as constraints, we build recommendation models from the successful behaviors of comparable users: the weight loss model is trained using the historical food consumption data of similar users who successfully lost weight. By combining such a goal-oriented recommendation model with a general model, the recommendations can be smoothly tuned toward the goal without disruptive food changes. We tested the approach on real data collected from a popular weight management app. It is shown that our recommendation approach can better predict the foods for test periods where the user truly meets the goal, than the typical existing approaches.
This paper presents a novel decomposition approach for measuring deterrence motives in dynamic oligopoly games. Our approach yields a formalized, scale-free, and interpretable measure of deterrence motives that informs researchers about the proportion for which deterrence motives account of all entry motives. In addition, the decomposition leads to a set of conditions for counterfactual analysis where hypothetical scenarios with deterrence motives eliminated can be explored. We illustrate the use of our measure and counterfactual by conducting an empirical case study about the dynamics of coffee chain stores in Toronto, Canada. The inferred deterrence motives suggest that a noticeable proportion of entry motives can be attributed to deterrence; it can be as high as 43% for the increasingly dominant coffee chain, Starbucks, in certain types of markets. Finally, counterfactual analysis confirms that deterrence motives are indeed associated with Starbucks’ aggressive presence as the number of its outlets and its market share are markedly lower once these motives are eliminated. This paper was accepted by Matthew Shum, marketing. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4864 .
This paper studies how spillover effects from competitors' choices affect a firm's decision to open a store. Using panel data from the United Kingdom's fast food industry, I propose and estimate a game of entry under incomplete information that incorporates spillover effects between firms' entry decisions. A positive spillover is identified for Burger King - increasing the stock of existing McDonald's by 1 outlet increases Burger King's estimated equilibrium probability of opening a new store by approximately 18 percentage points. Furthermore, the estimated model suggests that this spillover affects Burger King's variable profit, as opposed to its fixed cost of entry. It is less clear whether this externality matters for McDonald's.
This paper studies the impact of the online grocery retail channel on the variety and composition of shopping baskets. We use data from around one million shopping trips that capture both offline (brick-and-mortar) and online (Instacart) shopping behavior. We use unsupervised machine learning algorithms that are agnostic to the channel type to infer what constitutes a regular restocking shopping trip for each household. Our empirical analysis reveals that shopping basket variety, as measured by the number of categories purchased, is lower for online shopping trips and that the composition differs from offline trips in identifiable ways. We find that Instacart shopping baskets typically have 19.6% fewer fresh vegetable items and fewer items from impulse purchase categories that include candy (6.6%), bakery desserts (5.7%), and savory snacks (4.1%). Importantly, these fresh vegetables and impulse purchases are not picked up via alternative or additional shopping trips within a seven-day period. We show that these purchasing patterns are unlikely to be driven by price or assortment differences across the two channels or stock-outs due to Covid-19. Finally, we find that within a given household, the Instacart baskets are significantly more similar to each other than offline baskets, potentially suggesting a past-orders-shortcut mechanism behind our results.
We develop and estimate a dynamic game of strategic firm expansion and contraction decisions to study the role of firm size in future profitability and market dominance. Modeling firm size is important because retail chain dynamics are more richly driven by expansion and contraction than de novo entry or permanent exit. Additionally, anticipated size spillovers may influence the strategies of forward-looking firms, making it difficult to analyze the effects of size without explicitly accounting for these in the expectations and, hence, decisions of firms. Expansion may also be profitable for some firms while detrimental for others. Thus, we explicitly model and allow for heterogeneity in the dynamic link between firm size and profits as well as potential for persistent brand effects through firm-specific unobservable factors. As a methodological contribution, we surmount the hurdle of estimating the model by extending a two-step procedure that circumvents solving the game. The first stage combines semiparametric conditional choice probability estimation with a particle filter to eliminate the serially correlated unobservable components. The second stage uses a forward simulation approach to estimate the payoff parameters. Data on Canadian hamburger chains from their inception in 1970 to 2005 provide evidence of firm-specific heterogeneity in brand effects, size spillovers, and persistence in profitability. This heterogeneous dynamic linkage shows how McDonald’s becomes dominant and other chains falter as they evolve, thus affecting market structure and industry concentration. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2814 . This paper was accepted by J. Miguel Villas-Boas, marketing.
We investigate the role of heterogeneous peer effects in encouraging healthy and sustainable lifestyles. Our analysis revolves around one of the largest and most extensive databases about weight loss, which contains well over 10 million observations that track individual participants' meeting attendance and progress in a large national weight loss program. A few key findings emerge. First, while higher weight loss among average performing peers leads to lower future weight loss for an individual, the effect of the top weight loss performer among peers leads to greater future weight loss for that same individual. Second, the discouraging effects from average peers and encouraging effects from top performing peers are magnified for individuals who struggled with weight loss in the past. Third, the encouraging effect of top performers has a long-run impact on an individual's weight loss success. Finally, we provide suggestive evidence that the discrepancy between the top and average performer effects is not likely an artifact of salience or informativeness of top performers, but instead, driven by its positive impact on the motivation to accomplish weight loss goals. Given our empirical findings, we discuss managerial implications on meeting design.
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