Assessing the homogenization of urban land management with an application to US residential lawn care.

Changes in land use, land cover, and land management present some of the greatest potential global environmental challenges of the 21st century. Urbanization, one of the principal drivers of these transformations, is commonly thought to be generating land changes that are increasingly similar. An implication of this multiscale homogenization hypothesis is that the ecosystem structure and function and human behaviors associated with urbanization should be more similar in certain kinds of urbanized locations across biogeophysical gradients than across urbanization gradients in places with similar biogeophysical characteristics. This paper introduces an analytical framework for testing this hypothesis, and applies the framework to the case of residential lawn care. This set of land management behaviors are often assumed--not demonstrated--to exhibit homogeneity. Multivariate analyses are conducted on telephone survey responses from a geographically stratified random sample of homeowners (n = 9,480), equally distributed across six US metropolitan areas. Two behaviors are examined: lawn fertilizing and irrigating. Limited support for strong homogenization is found at two scales (i.e., multi- and single-city; 2 of 36 cases), but significant support is found for homogenization at only one scale (22 cases) or at neither scale (12 cases). These results suggest that US lawn care behaviors are more differentiated in practice than in theory. Thus, even if the biophysical outcomes of urbanization are homogenizing, managing the associated sustainability implications may require a multiscale, differentiated approach because the underlying social practices appear relatively varied. The analytical approach introduced here should also be productive for other facets of urban-ecological homogenization.

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Social-Cognitive Determinants of Success in Online Communities for Weight Management

Background: Online communities may be an effective, convenient, and relatively inexpensive intervention platform for individuals seeking assistance with weight management. Recent research suggests that these communities may be as effective as in-person treatments for weight management; however, very little is known about the characteristics that predict weight loss amongst those using an online community. Methods: Within a social-cognitive framework, we sought to identify the psychosocial characteristics that are associated with successful weight management for users of MyFitnessPal, a popular online community for weight management. We recruited participants who were new to the online community and asked them to complete 2 surveys (one at baseline and one 3 months later) that assessed various psychosocial constructs as well as self-reported height and weight. Results: Participants in our sample reported losing, on average, 4.55 kg during the 3-month time period. We found that engaging in weight control behaviors (e.g., monitoring food intake, weighing oneself, etc.) fully mediated the relationship between several of our variables of interest (i.e., baseline self-efficacy and perceived social support within the community) and weight loss. We also found that participants who expected to lose more weight at baseline were significantly more likely to have lost more weight at follow-up. Conclusions: On average, participants in our study lost a clinically meaningful amount of weight. Predictors of weight loss within this community included perceived support within the community (mediated by weight control behaviors), baseline self-efficacy (mediated by weight control behaviors), and baseline outcome expectations. Results of this study can ultimately serve to inform the design of future eHealth interventions for weight management.