Essays in Greenhouse/Nursery Economics

This dissertation focuses on the greenhouse and nursery industry in the United States. Two major issues are explored: irrigation and plant disease. The first two essays examine wireless soil-moisture sensor networks, an emerging technology that measures soil moisture and optimizes irrigation levels in real time. The first essay describes a study in which a nationwide survey of commercial growers was administered to generate estimates of grower demand and willingness to pay for sensor networks. We find that adoption rates for a base system and demand for expansion components are decreasing in price, as expected. The price elasticity of the probability of adoption suggests that sensor networks are likely to diffuse at a rate somewhat greater than that of drip irrigation. In the second essay, yields, time-to-harvest, and plant quality were analyzed to measure sensor network profitability. Sensor-based irrigation was found to increase revenue by 62% and profit by 65% per year. The third essay investigates greenhouse nursery growers’ response to a quarantine imposed on the west coast of the United States from 2002 to present for the plant pathogen that causes Sudden Oak Death. I investigate whether growers choose to 1) improve their sanitation practices, which reduces the underlying risk of disease without increasing the difficulty of detecting the pathogen, 2) increase fungicide use, which also prevents disease but makes existing infections much harder to detect, or 3) change their crop composition towards more resistant species. First, a theoretical model is derived to formalize hypotheses on grower responses to the quarantine, and then these predictions are empirically tested using several public data sources. I do not find evidence that growers improve their sanitation practices in response to the quarantine. I do, however, find evidence that growers heavily increase their fungicide use in response to a quarantine policy that requires visual (as opposed to laboratory) inspection for the disease before every crop shipment, suggesting that the quarantine may have the adverse effect of making the pathogen harder to identify. I also do find evidence that growers shift away from susceptible crops and towards resistant crops.

UNDERSTANDING CUSTOMER CHOICES IN SERVICE OUTSOURCING AND REVENUE MANAGEMENT

This dissertation investigates customer behavior modeling in service outsourcing and revenue management in the service sector (i.e., airline and hotel industries). In particular, it focuses on a common theme of improving firms’ strategic decisions through the understanding of customer preferences. Decisions concerning degrees of outsourcing, such as firms’ capacity choices, are important to performance outcomes. These choices are especially important in high-customer-contact services (e.g., airline industry) because of the characteristics of services: simultaneity of consumption and production, and intangibility and perishability of the offering. Essay 1 estimates how outsourcing affects customer choices and market share in the airline industry, and consequently the revenue implications from outsourcing. However, outsourcing decisions are typically endogenous. A firm may choose whether to outsource or not based on what a firm expects to be the best outcome. Essay 2 contributes to the literature by proposing a structural model which could capture a firm’s profit-maximizing decision-making behavior in a market. This makes possible the prediction of consequences (i.e., performance outcomes) of future strategic moves. Another emerging area in service operations management is revenue management. Choice-based revenue systems incorporate discrete choice models into traditional revenue management algorithms. To successfully implement a choice-based revenue system, it is necessary to estimate customer preferences as a valid input to optimization algorithms. The third essay investigates how to estimate customer preferences when part of the market is consistently unobserved. This issue is especially prominent in choice-based revenue management systems. Normally a firm only has its own observed purchases, while those customers who purchase from competitors or do not make purchases are unobserved. Most current estimation procedures depend on unrealistic assumptions about customer arriving. This study proposes a new estimation methodology, which does not require any prior knowledge about the customer arrival process and allows for arbitrary demand distributions. Compared with previous methods, this model performs superior when the true demand is highly variable.