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The purpose of this dissertation was to investigate the attitudes of business and advertising executives, civic leaders, and individuals towards the perceived role of standardized or adapted international advertising appeals in promoting the 1996 Atlanta Centennial Olympic Games. The study was descriptive in design and used both secondary and primary data. The findings suggest that the 1996 Olympic Games should be advertised in other countries using culturally meaningful themes adapted to reflect the aesthetics, language, customs and educational level of the target audience. Conversely, Olympic advertising themes should not contain religious messages or be standardized. These findings carry implications for the International Olympic Committee, the World Cup Soccer Federation, Tourists Boards, Convention Bureaus, Chambers of Commerce, as well as scholars and practitioners who wish to advocate employing adapted or standardized advertising messages to culturally diverse target audience.

This thesis entitled "Some Economic and Historical Aspects of the Federal Income Tax Laws" with special emphasis on the 1928 Revenue Act, is divided into three main divisions, namely, first, historical development of federal income taxation in United States, second, income, as viewed by the economist, accountant, and the court, and lastly, the deductions permitted to be taken from gross income in arriving at net taxable income.

The subject of unemployment insurance legislation is a timely and highly controversial one. In order to evaluate the movement it is desirable to understand the development of the theory and to investigate the early attempts to secure unemployment legislation in America and in Europe.

An attempt has been made by the author in the pages which follow to show the development in a rather detailed manner of the American business corporation previous to and through the eighteenth century. The early chapters of this work have seemed advisable because they give the reader a general background which the author believes is beneficial in interpreting the latter part of the work.

It is the opinion of many competent students of social problems that, even though this country experience an economic recovery during the coming years that surpasses the most optimistic forecasts, we shall still be confronted with an army of unemployed numbering in excess of 5 million persons.

<p>This dissertation studies the integration of material and financial flows in supply chains, with the goal of examining the impact of cash flows on the individual firm's decision making and the overall supply chain efficiency. We develop analytical models to provide effective policy recommendations and derive managerial insights.</p><p>We first consider a credit-constrained firm that orders inventory to satisfy stochastic demand in a finite horizon. The firm provides trade credit to the customer and receives it from the supplier. A default penalty is incurred on the unfulfilled payment to the supplier. We utilize an accounting concept of working capital to obtain optimal and near-optimal inventory policies. The model enables us to suggest an acceptable purchasing price offered in the supplier's trade credit contract, and to demonstrate how liquidity provision can mitigate the bullwhip effect. We then study a joint inventory and cash management problem for a multi-divisional supply chain. We consider different levels of cash concentration: cash pooling and transfer pricing. We develop the optimal joint inventory replenishment and cash retention policy for the cash pooling model, and construct cost lower bounds for the transfer pricing model. The comparison between these two models shows the value of cash pooling, although a big portion of this benefit may be recovered through optimal transfer pricing schemes. Finally, we build a supply chain model to investigate the material flow variability without cash constraint. Our analytical results provide conditions under which the material bullwhip effect exists. These results can be extended to explain the similar effect when financial flows are involved. In sum, this dissertation demonstrates the importance of working capital and financial integration in supply chain management.</p> / Dissertation

<p>\abstract</p><p>Managers constantly make decisions that depend, at least in part, on what they believe their competitors (or customers, or employees) will do in response. These judgments are susceptible to error. Indeed, behavioral research suggests a widespread bias towards underestimating others. To explore the ramifications of such an intuitively irrational bias, we provide a theoretical model that compares the long-run effects of consistent underestimation bias with those of consistent overestimation bias across different competitive contexts relevant to marketing managers. In the first set of analyses, we derive analytic equations to calculate the relative expected payoffs associated with conditions of overestimation bias, underestimation bias, and no bias and discern trends as a function of environmental features including game complexity and player skill levels. In the second set of analyses, we derive equations for the relative effort costs associated with each of the three bias conditions as a function of the relevant game and player parameters. We then combine the results of the expected payoffs and effort costs to determine the relative net expected payoffs associated with each bias condition as a function of game and player parameters. In the third set of analyses, we relax many of the assumptions present in the first analysis and test the relationships of interest across additional contexts including risk aversion, power imbalance, and opponent arrogance. The results across all analyses are summarized by fifteen propositions which show that, when effort is at all costly, underestimation will provide the best net expected payoff when games are above some critical level of complexity as well as when opponents have above some critical minimum level of skill. The range of underestimation's advantage compared to overestimation can be increased in contexts with high first mover advantage, when payoffs are cumulative over time, when the player is risk averse, when there is an imbalance in power between the players, and when the opponent exhibits arrogance. Furthermore, underestimation can outperform overestimation even when effort is not costly when there is a power imbalance between the players or when the player exhibits certain risk attitudes. The results provide theoretical support for the ecological rationality of underestimation bias by showing it to be advantageous under many conditions, particularly in comparison to overestimation bias. It also provides managers with prescriptive insights regarding when any opponent skill estimation error is more vs. less harmful, and when managers may fare better in the long run if they don't spend too much time trying to think through the competition's eyes..</p> / Dissertation

<p>This dissertation studies how organizations, when solving a specific problem, identify a set of potential solutions which we call "Search Space." By drawing from evolutionary theory and related literatures on strategic change, scholars have demonstrated differences in search mechanisms that explain how organizations choose solutions. However, we still face unanswered questions in understanding how organizations decide where to search, including how organizations identify a set of potential solutions or Search Space. This dissertation defines the concept of Search Space and identifies three factors - uncertainty, prior top managerial attention, and prior experience -that drive differences in Search Space. Additionally, this dissertation starts to disentangle why some firms' top managers are predisposed to paying more attention to new strategic areas by investigating the relationship between uncertainty and top managerial attention. Hypotheses first testing the effect of uncertainty, prior top managerial attention, and prior experience on size of Search Space, and second testing the effect of uncertainty on changes in top managerial attention are tested using data describing the U.S. renewable electricity sector from 2000 to 2010. We conduct both a cross-sectional analysis using data collected though a multiple respondent survey and a panel data analysis by tracking firms' memberships in renewable electricity trade groups. We find that uncertainty and prior top managerial attention increase size of Search Space, but related prior experience reduces size of Search Space. Additionally, uncertainty positively changes attention of top managers at headquarter units, but not at subsidiary units, towards renewable electricity. These results contribute to our understanding of how organizations start solving problems by deciding where to search; how the boundaries of top managerial attention direct Search Space; and how different types of top managers interpret uncertainty. Empirically, these results have important implications for how renewable policies should be structured and how firms develop new projects in the U.S. renewable electricity sector.</p> / Dissertation

This thesis examines two complex, dynamic problems by employing the theory of Markov Decision Processes (MDPs). Chapters 2 and 3 consider assemble-to-order (ATO) inventory systems. An ATO system consists of several components and several products, and assembles products as demand is realized; it is becoming increasingly popular since it provides greater flexibility in manufacturing at a reasonable cost. This work contributes to the ATO research stream by characterizing optimal inventory replenishment and allocation policies. Chapter 4 examines the new product development (NPD) process with scarce resources and many projects in parallel, each lasting several periods, in the face of uncertainty. This study advances the NPD literature by revealing that optimal project selection and resource allocation decisions are congestion-dependent. Below, I elaborate on the novel optimal policies and structural results I obtain using MDP formulations, which is the overarching theme of the thesis. In Chapter 2, I consider generalized ATO “M-systems" with multiple components and multiple products. These systems involve a single “master" product which uses multiple units from each component, and multiple individual products each of which consumes multiple units from a different component. Such systems are common for manufacturers selling an assembled product as well as individual spare parts. I model these systems as infinite-horizon MDPs under the discounted cost criterion. Each component is produced in batches of fixe size in a make-to stock fashion; batch sizes are determined by individual product sizes. Production times are independent and exponentially distributed. Demand for each product arrives as an independent Poisson process. If not satisfied immediately upon arrival, these demands are lost. Therefore the state of the system can be described by component inventory levels. A control policy specifies when a batch of components should be produced (i.e., inventory replenishment), and whether an arriving demand for each product should be satisfied (i.e.,inventory allocation). The convexity property that has been largely used to characterize optimal policies in the MDP literature may fail to hold in our case. Therefore I introduce new functional characterizations for submodularity and supermodularity restricted to certain lattices of the state space. The optimal cost function satisfies these new characterizations: The state space of the problem can be partitioned into disjoint lattices such that, on each lattice, (a) it is optimal to produce a batch of a particular component if and only if the state vectors less than a certain threshold associated with that component, and (b) it is optimal to fulfill a demand of a particular product if and only if the state vector is greater than or equal to a certain threshold associated with that product. I refer to this policy as a lattice-dependent base-stock and lattice-dependent rationing (LBLR) policy. I also show that if the optimization criterion is modified to the average cost rate, LBLR remains optimal. Chapter 2 makes three important contributions. First, this is the first study that establishes the optimal inventory replenishment and allocation policies for M-systems. Second, this study is the first to characterize the optimal policies for any ATO problem when different products may use the same component in different quantities. Third, I introduce new functional characterizations restricted to certain lattices of the state space, giving rise to an LBLR policy. In Chapter 3, I evaluate the use of an LBLR policy for general ATO systems as a heuristic. I numerically compare the globally optimal policy to LBLR and two other heuristics from the literature: a state-dependent base-stock and state-dependent rationing (SBSR) policy, and a fixed base-stock and fixed rationing (FBFR) policy. Taking the average cost rate as the performance criterion, I develop a linear program to find the globally optimal cost, and Mixed Integer Programming formulations to find the optimal cost within each heuristic class. I generate more than 1800 instances for the general ATO problem, not restricted to the assumptions of Chapter 2, such as the M-system product structure. Interestingly, LBLR yields the globally optimal cost in all instances, while SBSR and FBFR provide solutions within 2.7% and 4.8% of the globally optimal cost, respectively. These numerical results also provide several insights into the performance of LBLR relative to other heuristics: LBLR and SBSR perform significantly better than FBFR when replenishment batch sizes imperfectly match the component requirements of the most valuable or most highly demanded product. In addition, LBLR substantially outperforms SBSR if it is crucial to hold a significant amount of inventory that must be rationed. Based on the numerical findings in Chapter 3, future research could investigate the optimality of LBLR for ATO systems with general product structures. However, as I construct counter-examples showing that submodularity and supermodularity { which are used to prove the optimality of LBLR in Chapter 2 { need not hold for general ATO systems, showing the optimality of LBLR for general ATO systems will likely require alternate proof techniques. In Chapter 4, I study the problem of project selection and resource allocation in a multistage new product development (NPD) process with stage-dependent resource constraints. As in Chapters 2 and 3, I model the problem as an infinite-horizon MDP, specifically under the discounted cost criterion. Each NPD project undergoes a different experiment at each stage of the NPD process; these experiments generate signals about the true nature of the project. Experimentation times are independent and exponentially distributed. Beliefs about the ultimate outcome of each project are updated after each experiment according to a Bayesian rule. Projects thus become differentiated through their signals, and all available signals for a project determine its category. The state of the system is described by the numbers of projects in each category. A control policy specifies, given the system state, how to utilize the resources at each stage, i.e., the projects (i) to experiment at each stage, and (ii) to terminate. I characterize the optimal control policy as following a new type of strategy, state-dependent on-congestive promotion (SDNCP), for two different special cases of the general problem: (a)when there is a single informative experiment and projects are not terminated, or (b) when there are multiple uninformative experiments. An SDNCP policy implies that, at each stage, it is optimal to advance a project with the highest expected reward to the next stage if and only if the number of projects in each successor category is less than a state-dependent threshold. In addition, I show that threshold values decrease in a non-strict sense as a later stage becomes more congested or as an earlier stage becomes less congested. (A stage becomes “more congested" with an increase in the number of projects at this stage or with an increase in the expected reward of any project at this stage.) An SDNCP policy can be used as a heuristic for the general problem. I support the outstanding performance of an SDNCP policy in the general case through a numerical study. These findings highlight the importance of taking into account congestion in optimal portfolio strategies.