Browsing by Subject "Business Administration, General"

I examine the relation between a corporate commitment to increased disclosure and measures of liquidity, information asymmetry, and cost of equity capital. Relative to prior research on voluntary disclosure, I use a composite, ex ante measure of commitment based in social psychology and measure commitment using characteristics of earnings announcement disclosures. Prior to Regulation Fair Disclosure (Reg FD) I find that commitment to increased disclosure is negatively related to bid-ask spreads, probability of informed trade (PIN) scores, and implied cost of capital estimates. Further analysis reveals that the disclosure of balance sheet information in earnings releases is significantly related to spreads and PINs, regardless of firms' conference call behavior, while the combination of consistent open calls and disclosure of balance sheet information in earnings releases yields the most significant results for cost of capital. After the effective date of Reg FD I find that commitment is negatively related to PIN scores and implied cost of capital estimates, but not related to bid-ask spreads. Further analysis reveals that the disclosure of balance sheet information in earnings releases is significantly related to PINs and cost of capital, regardless of firms' conference call behavior.

We present two game theoretic models on resource allocation in epidemic control and waiting time competition in service management.

The first model considers the drug allocation problem when facing the outbreak of an international influenza pandemic. The drugs are for prophylactic use. Since drug stockpiles are presently scattered in different countries, the outbreak of an epidemic gives rise to a game in which each country must make decisions about how best to allocate its own stockpile in order to protect its population. We develop a two-period multivariate Reed-Frost model to represent the spread of the epidemic within and across countries at its onset. We show that for small probability of between-country infections, the underlying game is supermodular and that a unique Pareto optimal Nash equilibrium exists. Further, we identify sufficient conditions under which the optimal solution of a central planner constitutes a Pareto improvement over the decentralized equilibrium.

The second model investigates the impact of reference effect on firms' waiting

time competition. We consider two firms (modeled as M/M/1 queueing facilities)

announcing the expected waiting times to attract customers. Customers' joining

decisions depend not only on the announced waiting times, but also on the reference effect, as demonstrated by the psychology literature. Reference effect, modeled in the spirit of prospect theory, is a "kinked" function of the gap between customers' expectation and perception of the waiting time, where the expectation is formed by both firms'announcements. We prove the existence of a unique (Pareto optimal) Nash equilibrium in the presence of reference effect. We show that comparing to the no reference effect case, the equilibrium waiting times are shorter and firms tend to announce a same waiting time.

Many types of services are provided using some equipment or machines, e.g. transportation systems using vehicles. Designs of these systems require capacity decisions, e.g., the number of vehicles. In my dissertation, I use many-server and conventional heavy-traffic limit theory to derive asymptotically optimal capacity decisions, giving the desired level of delay and availability performance with minimum investment. The results provide near-optimal solutions and insights to otherwise analytically intractable problems.

The dissertation will comprise two essays. In the first essay, &ldquoAsymptotic Analysis of Delay-based Performance Metrics and Optimal Capacity Decisions for the Machine Repair Problem with Spares,&rdquo I study the M/M/R machine repair problem with spares. This system can be represented by a closed queuing network. Applications include fleet vehicles' repair and backup capacity, warranty services' staffing and spare items investment decisions. For these types of systems, customer satisfaction is essential; thus, the delays until replacements of broken units are even more important than delays until the repair initiations of the units. Moreover, the service contract may include conditions on not only the fill rate but also the probability of acceptable delay (delay being less than a specified threshold value).

I address these concerns by expressing delays in terms of the broken-machines process; scaling this process by the number of required operating machines (or the number of customers in the system); and using many-server limit theorems (limits taken as the number of customers goes to infinity) to obtain the limiting expected delay and probability of acceptable delay for both delay until replacement and repair initiation. These results lead to an approximate optimization problem to decide on the repair and backup-capacity investment giving the minimum expected cost rate, subject to a constraint on the acceptable delay probability. Using the characteristics of the scaled broken-machines process, we obtain insights about the relationship between quality of service and capacity decisions. Inspired by the call-center literature, we categorize capacity level choice as efficiency-driven, quality-driven, or quality- and efficiency-driven. Hence, our study extends the conventional call center staffing problem to joint staffing and backup provisioning. Moreover, to our knowledge, the machine-repair problem literature has focused mainly on mean and fill rate measures of performance for steady-state cost analysis. This approach provides complex, nonlinear expressions not possible to solve analytically. The contribution of this essay to the machine-repair literature is the construction of delay-distribution approximations and a near-optimal analytical solution. Among the interesting results, we find that for capacity levels leading to very high utilization of both spares and repair capacity, the limiting distribution of delay until replacement depends on one type of resource only, the repair capacity investment.

In the second essay, &ldquoDiffusion Approximations and Near-Optimal Design of a Make-to-Stock Queue with Perishable Goods and Impatient Customers,&rdquo I study a make-to-stock system with perishable inventory and impatient customers as a two-sided queue with abandonment from both sides. This model describes many consumer goods, where not only spoilage but also theft and damage can occur. We will refer to positive jobs as individual products on the shelf and negative jobs as backlogged customers. In this sense, an arriving negative job provides the service to a waiting positive job, and vice versa. Jobs that must wait in queue before potential matching are subject to abandonment. Under certain assumptions on the magnitude of the abandonment rates and the scaled difference between the two arrival rates (products and customers), we suggest approximations to the system dynamics such as average inventory, backorders, and fill rate via conventional heavy traffic limit theory.

We find that the approximate limiting queue length distribution is a normalized weighted average of two truncated normal distributions and then extend our results to analyze make-to-stock queues with/without perishability and limiting inventory space by inducing thresholds on the production (positive) side of the queue. Finally, we develop conjectures for the queue-length distribution for a non-Markovian system with general arrival streams. We take production rate as the decision variable and suggest near-optimal solutions.

Organizational Capital Budgeting Model (OCBM) is a general theory of capital budgeting that incorporates traditional capital budgeting theories and the consideration about firm's information/ organization structure. The traditional financial capital budgeting model is a special case of OCBM. Therefore, OCBM not only broadens the traditional model, but also explains the heterogeneous behaviors of firms using quasi/non-financial version of capital budgeting. I demonstrate the validity of OCBM with multiple research methods. The field studies about Asian conglomerates are carefully constructed. The conglomerates are important dataset to study organizational decision making because of their size, scope, controversial behaviors and global presence.

Probability forecasts play an important role in many decision and risk analysis applications. Research and practice over the years have shown that the shift towards distributional forecasts provides a more accurate and appropriate means of capturing risk in models for these applications. This means that mathematical tools for analyzing the quality of these forecasts, may it come from experts, models or data, become important to the decision maker. In this regard, strictly proper scoring rules have been widely studied because of their ability to encourage assessors to provide truthful reports. This dissertation contributes to the scoring rule literature in two main areas of assessment - probability forecasts and quantile assessments.

In the area of probability assessment, scoring rules typically studied in the literature, and commonly used in practice, evaluate probability assessments relative to a default uniform measure. In many applications, the uniform baseline used to represent some notion of ignorance is inappropriate. In this dissertation, we generalize the power and pseudospherical family of scoring rules, two large parametric families of commonly-used scoring rules, by incorporating the notion of a non-uniform baseline distribution for both the discrete and continuous cases. With an appropriate normalization and choice of parameters, we show that these new families of scoring rules relate to various well-known divergence measures from information theory and to well-founded decision models when framed in an expected utility maximization context.

In applications where the probability space considered has an ordinal ranking between states, an important property often considered is sensitivity to distance. Scoring rules with this property provide higher scores to assessments that allocate higher probability mass to events “closer” to that which occurs based on some notion of distance. In this setting, we provide an approach that allows us to generate new sensitive to distance strictly proper scoring rules from well-known strictly proper binary scoring rules. Through the use of the weighted scoring rules, we also show that these new scores can incorporate a specified baseline distribution, in addition to being strictly proper and sensitive to distance.

In the inverse problem of quantile assessment, scoring rules have not yet been well-studied and well-developed. We examine the differences between scoring rules for probability and quantile assessments, and demonstrate why the tools that have been developed for probability assessments no longer encourage truthful reporting when used for quantile assessments. In addition, we shed light on new properties and characterizations for some of these rules that could guide decision makers trying to choosing an appropriate scoring rule.