ABSTRACT Much is known about the process of sponsorship selection with respect to the key personnel involved in the buying decision and also about the strategic objectives for sponsorship. However, few investigations have focused on how sponsors assess the relative value of different sponsorship activities. This research project examined sponsorship decision-making with reference to the way managers discuss, think, and act when evaluating a sponsorship property for the first time. It focuses in particular on understanding how sponsors make decisions when selecting a new activity; how perceptions of risk influence their choice behaviours; the relative value they attribute to different sponsorship features; and how individual factors influence their choice of sponsorship domain. This research program considered these issues from three dimensions (1) linguistically (i.e. how firms publicly describe and explain their sponsorship selection procedures to others); (2) cognitively (i.e. how sponsorship experts describe and rationalise the decision process); and (3) conatively (i.e. how sponsorship managers report they behave when making sponsorship selection decisions). First, Study 1 explored the linguistic dimension of sponsorship selection through a content analysis of the sponsorship policies and guidelines of 298 global, national and local firms. The content analysis of these documents was conducted using Leximancer text analysis software. The analysis revealed six attributes were particularly important. These were the cost of sponsorship rights fees; the capacity of the sponsorship to achieve brand marketing objectives; the opportunities for brand exposure; the sharing of values between partners; the type/domain of the sponsorship activity; and its geographic reach. Firms avoided activities likely to damage their corporate or brand image by alienating sections of the community or by violating social norms. Next, Study 2 explored the cognitive dimension of sponsorship through a series of in-depth interviews with 16 sponsors and 20 properties. Interviews revealed that while practitioners supported the importance of attributes similar to those identified in Study 1, they placed more emphasis on the duration of the sponsorship agreement, the partner’s reputation and sponsorship management ability, and on the level of involvement, and less emphasis on shared values and geographic reach. Risk assessment was implicit in their due diligence practices. Risk mitigation strategies included risk avoidance, risk reduction, risk retention, and risk transfer. Study 3 examined the conative dimension of the sponsorship selection process using a full-profile choice-based conjoint (CBC) experiment completed by 196 sponsorship managers. The respondents evaluated 17 sets of fully-randomised fictitious sponsorship proposals constructed using the attributes identified in the previous two studies. Hierarchical Bayes (HB) analysis showed that the degree of fit with brand objectives, the duration of the sponsorship, and the perceived quality of the partner relationship exerted the strongest influence on sponsor preferences. Specifically, sponsors placed the highest value on sponsorships that offered a very high fit with their brand objectives, a one-year agreement, a good partner relationship, were cause-related, had a State-wide reach, involved a title sponsorship, a combination of cash and in-kind payment, and offered print media exposure. Finally, to examine the influence of individual factors (i.e. gender, the level of decision-making authority, and experience in decision-making) on the choice of sponsorship domain (i.e. sport sponsorship, arts sponsorship, cause-related sponsorship, and celebrity endorsement), three sponsorship simulations were conducted as part of Study 3 using Share of Preference modelling. The results showed that cause-related sponsorship was the most strongly preferred domain in each of the three models, whereas celebrity endorsement was the least preferred. While female managers were indifferent to arts or sport sponsorship, male managers showed a strong preference for sport sponsorship over arts sponsorship. Managers were less interested in sport sponsorship than more senior executives. The preferences of managers with the least experience were consistent with those of highly-experienced sponsors. However, managers with 11-15 years experience showed much less interest in sport sponsorship than others. Conceptually, this program of research allowed for the development of a decision-making model that provides the basis for future investigations of sponsorship value. For the practitioner, the results of this research support previous findings about the significance of sponsorship activities having a good fit with the sponsor’s brand objectives. As well, properties with a good reputation for building high-quality sponsorship relationships will find favour with new sponsors.

This project addresses the problem of comparing the offensive abilities of players from different eras in Major League Baseball (MLB). We will study players from the perspective of an overall offensive summary statistic that is highly linked with scoring runs, or the Berry Value. We will build an additive model to estimate the innate ability of the player, the effect of the relative level of competition of each season, and the effect of age on performance using piecewise age curves. Using Hierarchical Bayes methodology with Gibbs sampling, we model each of these effects for each individual. The results of the Hierarchical Bayes model permit us to link players from different eras and to rank the players across the modern era of baseball (1900-2004) on the basis of their innate overall offensive ability. The top of the rankings, of which the top three were Babe Ruth, Lou Gehrig, and Stan Musial, include many Hall of Famers and some of the most productive offensive players in the history of the game. We also determine that trends in overall offensive ability in Major League Baseball exist based on different rule and cultural changes. Based on the model, MLB is currently at a high level of run production compared to the different levels of run production over the last century.

Recollection is a pattern completion process that enables retrieval of arbitrarily associated information following minimal study. These attributes enable recollection to support retrieval of many kinds of mnemonic representations, from highly associative contextual information to very specific low-level representations. However, recollection is typically studied in the context of declarative memory tasks, in which participants exhibit recollection by explicitly reporting on the recollected information. Is it the case that recollection is limited to declarable representations, or is it a more general process that occurs for any representation? Two experiments and a novel analysis technique are presented to answer this question. The results suggest that recollection is not limited to declarable representations. These results argue against theories of recognition memory that restrict the representational input allowed to mnemonic processes; mnemonic processes in general may act on arbitrary representations.

<p>Infusing models for animal movement with more behavioral realism has been a goal of movement ecologists for several years. As ecologists have begun to collect more and more data on animal distribution and abundance, a clear need has arisen for more sophisticated analysis. Such analysis could include more realistic movement behavior, more information on the organism-environment interaction, and more ways to separate observation error from process error. Because landscape ecologists and behavioral ecologists typically study these same themes at very different scales, it has been proposed that their union could be productive for all (Lima and Zollner, 1996). </p><p>By understanding how animals interact with their land- and seascapes, we can better understand how species partition up resources are large spatial scales. Accordingly I begin this dissertation with a large spatial scale analysis of distribution data for marine mammals from Nova Scotia through the Gulf of Mexico. I analyzed these data in three separate regions, and in the two data-rich regions, find compelling separation between the different communities. In the northernmost region, this separation is broadly along diet based partitions. This research provides a baseline for future study of marine mammal systems, and more importantly highlights several gaps in current data collections.</p><p>In the last 6 years several movement ecologists have begun to imbue sophisticated statistical analyses with increasing amounts of movement behavior. This has changed the way movement ecologists think about movement data and movement processes. In this dissertation I focus my research on continuing this trend. I reviewed the state of movement modeling and then proposed a new Bayesian movement model that builds on three questions of: behavior; organism-environment interaction; and process-based inference with noisy data.</p><p>Application of this model to two different datasets, migrating right whales in the NW Atlantic, and foraging monk seals in the Northwest Hawaiian Islands, provides for the first time estimates of how moving animals make choices about the suitability of patches within their perceptual range. By estimating parameters governing this suitability I provide right whale managers a clear depiction of the gaps in their protection in this vulnerable and understudied migratory corridor. For monk seals I provide a behaviorally based view into how animals in different colonies and age and sex groups move throughout their range. This information is crucial for managers who translocate individuals to new habitat as it provides them a quantitative glimpse of how members of certain groups perceive their landscape.</p><p>This model provides critical information about the behaviorally based movement choices animals make. Results can be used to understand the ecology of these patterns, and can be used to help inform conservation actions. Finally this modeling framework provides a way to unite fields of movement ecology and graph theory.</p> / Dissertation

Conjoint analysis is one of the most important tools to support product development, pricing and positioning decisions in management practice. For this purpose various models have been developed. It is widely accepted that models that take consumer heterogeneity into account, outperform aggregate models in terms of hold-out tasks. The aim of our study is to investigate empirically whether predictions of choice-based conjoint models which incorporate heterogeneity can successfully be generalized to a whole market. To date no studies exist that examine the real world performance of choice-based conjoint models by use of aggregate scanner panel data. Our analysis is based on four commercial choice-based conjoint pricing studies including a total of 43 stock keeping units (SKU) and the corresponding weekly scanning data for approximately two years. An aggregate model serves as a benchmark for the performance of two models that take heterogeneity into account, hierarchical Bayes and latent class. Our empirical analysis demonstrates that, in contrast to the performance using hold-out tasks, the real world performance of hierarchical Bayes and latent class is similar to the performance of the aggregate model. Our results indicate that heterogeneity cannot be generalized to a whole market and suggest that aggregate models are sufficient to predict market shares. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

In many studies, choice based conjoint analysis is used to build a market simulator to develop marketing strategies; i.e., shares-of-preference are taken as market share forecasts. However, conjoint data are collected in interview situations, which may differ considerably from real shopping behavior. In this paper, we test the internal and external validity of four commercial choice based conjoint pricing studies including a total of 43 brands. We use conjoint and sales data to assess the relative performance of two modern approaches to estimate conjoint parameters: the segmentation based Latent Class model and the individual level Hierarchical Bayes approach. Our paper confirms previous results of the internal superiority of the Hierarchical Bayes approach. The main result of our investigation is that internal validity does not predict external validity and that Latent Class shows the same real world performance as Hierarchical Bayes. Both models show an average error of 4.2% in market share level prediction and a correlation of 69% between conjoint forecasts and real market shares. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

This dissertation consists of three essays studying the economics of risky health behaviors. Essay 1 estimates the effects of Graduated Driver Licensing (GDL) restrictions on weight status among adolescents aged 14 to 17 in the U.S. The findings suggest that a night curfew significantly raises adolescents’ probability of being “overweight or obese” by 1.32 percentage points, corresponding to an increase in “overweight or obesity” rate of 4.8%. A night curfew combined with a passenger restriction increases this rate by 5.8%. Overall, I estimate that nearly 16% of the rise in “overweight or obesity” rate among teenagers aged 14 to 17 in the U.S from 1999 to 2015 can be explained by the presence of the GDL restrictions. In addition, the restrictions reduce teenagers’ exercise frequency while increasing their time spent watching TV, which may help to explain the adverse effects on obesity. Essay 2 exploits the effects of the Graduated Driver Licensing (GDL) restrictions on youth smoking and drinking. It finds that being subject to minimum entry age, a learner stage, or only a night curfew has no statistically significant effect whereas, interestingly, a night curfew combined with a passenger restriction reduces youth smoking and drinking. The estimated effects become more statistically significant and larger in magnitude in the medium run, which is in line with the addictive nature of these substances. Essay 3 investigates the underlying causes of suicide. It uses data from the U.S. at the county level and the primary methodology is a two-level Bayesian hierarchical model with spatially correlated random effects. The results show that the significant effects of observable factors on suicides found by earlier research may partially stem from excluding small area effects and time trends, without controlling for which the true contribution of unobserved propensities and time trends can be hidden within observable factors. Most importantly, a lot can be learned from unobserved yet persistent propensity toward suicide captured by the spatially correlated county specific random effects. Resources should be allocated to counties with high suicide rates, but also counties with low raw suicide rates but high unobserved propensities of suicide.

Cette thèse est organisée en trois chapitres. Chaque article analyse les déviations systématiques des décideurs par rapport aux prédictions économiques classiques dans certaines expériences bien connues. Les agents s’écartent de la voie optimale et explorent ou exploitent de manière excessive dans le problème du bandit manchot, ils exigent des taux d’intérêt bien plus élevés par rapport aux taux du marché financier afin de reporter leurs dépenses lorsqu’ils prennent des décisions de choix intertemporel, et ils ne se contentent pas de recevoir des petites sommes d’argent, même si, objectivement, ils devraient accepter cette offre, dans des expériences de négociation comme le jeu de l’ultimatum. Ces soi-disant «irrégularités» sont documentées dans les trois essais de thèse. Le essaies représentent une première étape afin de formuler des stratégies adaptées au profile psychologique de chaque individu, nécessaires pour surmonter les biais de décision. / This dissertation is organized in three chapters. Each chapter analyzes decision makers’ systematic deviations from economic predictions in well-known experiments. People deviate from the optimal path and excessively explore or exploit in n-armed bandit games, demand interest rates well above financial market averages in order to defer consumption in intertemporal choice settings, and do not settle for receiving small amounts of money, even though they would be better off objectively, in bargaining games such as the ultimatum game. Such “irregularities” are documented in the three dissertation essays. The essays are intended as a first step to formulate individual specific, customized decision aids, useful to overcome such decision biases.

Direct marketing problems have been intensively reviewed in the marketing literature recently, such as purchase frequency and time, sales profit, and brand choices. However, modeling the customer response, which is an important issue in direct marketing research, remains a significant challenge. This thesis is an empirical study of predicting customer response to direct marketing and applies a Bayesian approach, including the Bayesian Binary Regression (BBR) and the Hierarchical Bayes (HB). Other classical methods, such as Logistic Regression and Latent Class Analysis (LCA), have been conducted for the purpose of comparison. The results of comparing the performance of all these techniques suggest that the Bayesian methods are more appropriate in predicting direct marketing customer responses. Specifically, when customers are analyzed as a whole group, the Bayesian Binary Regression (BBR) has greater predictive accuracy than Logistic Regression. When we consider customer heterogeneity, the Hierarchical Bayes (HB) models, which use demographic and geographic variables for clustering, do not match the performance of Latent Class Analysis (LCA). Further analyses indicate that when latent variables are used for clustering, the Hierarchical Bayes (HB) approach has the highest predictive accuracy.

A large body of literature studies the issues of the option price and other ex-ante welfare measures under the microeconomic theory to valuate reductions of risks inherent in environment and human health. However, it does not offer a careful discussion of how to estimate risk reduction values using data, especially the modeling and estimating individual perceptions of risks present in the econometric models. The central theme of my dissertation is the approaches taken for the empirical estimation of probabilistic risks under alternative assumptions about individual perceptions of risk involved: the objective probability, the Savage subjective probability, and the subjective distributions of probability. Each of these three types of risk specifications is covered in one of the three essays. The first essay addresses the problem of empirical estimation of individual willingness to pay for recreation access to public land under uncertainty. In this essay I developed an econometric model and applied it to the case of lottery-rationed hunting permits. The empirical result finds that the model correctly predicts the responses of 84% of the respondents in the Maine moose hunting survey. The second essay addresses the estimation of a logit model for individual binary choices that involve heterogeneity in subjective probabilities. For this problem, I introduce the use of the hierarchical Bayes to estimate, among others, the parameters of distribution of subjective probabilities. The Monte Carlo study finds the estimator asymptotically unbiased and efficient. The third essay addresses the problem of modeling perceived mortality risks from arsenic concentrations in drinking water. I estimated a formal model that allows for ambiguity about risk. The empirical findings revealed that perceived risk was positively associated with exposure levels and also related individuating factors, in particular smoking habits and one’s current health status. Further evidence was found that the variance of the perceived risk distribution is non-zero. In all, the three essays contribute methodological approaches and provide empirical examples for developing empirical models and estimating value of risk reductions in environment and human health, given the assumption about the individual’s perceptions of risk, and accordingly, the reasonable specifications of risks involved in the models.