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<p>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.
</p><p>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. </p><p>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.</p><p>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.</p>
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