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Doctor of Philosophy / Department of Agricultural Economics / Jason S. Bergtold / Timothy J. Dalton / Adoption of technological improvements are crucial to increase agricultural productiviy to help reduce poverty by obtaining higher farm incomes due to higher productivity and lower production costs. However, the introduction of new agricultural technologies has not always been successful or had diffuse adoption. Factors that determine farmers’ adoption decisions are: 1) farm and farmers' characteristics; 2) technology attributes, and 3) the farming objective. Understanding these factors and how they affect adoption of new technologies on the farm is crucial to assure higher levels of adoption. The over all purpose of this thesis is to explore the adoption process of new technologies and practices by farmers. This is accomplished through three essays to meet the objectives of the thesis. The purpose of the first essay was to evaluate whether or not farmers in the western U.S. are willing to grow specialized oilseed crops that could be used for certified hydrotreated renewable jet (HRJ) fuel production and incorporate them into existing wheat-based production systems under contract. Results indicate that providing oilseeds crops and contracts with desired attributes and features would positively affect farmers' decisions to incorporate oilseed crops into their rotation system. Preferred seed and contract attributes that may affect a farmer’ adoption decision differ across different geographic regions of the U.S. The second essay focused on identifying factors that impact participation and farmers' decision to adopt soil conservation and fertilization management practices for cassava producers in Thailand and Vietnam. Results indicate that asset ownership and cassava yield positively influence participation. Adoption of new practices was positively linked to farmers’ participation in training activities, use of fish ponds (as a measure of alternative agricultural practices), presence of a nearby starch factory, and slope of the land. Finally, the purpose of the third essay was to examine extension educators' characteristics that affect educators' selection decision of outreach methods in the U.S. This essay examines the diffusion process that impacts adoption of best management practices by farmers. The decision extension educators make for selecting a teaching method is affected by the relationship between the objectives of the learning process and the characteristics of the teaching method.

The eCommerce industry introduced new business principles, as well as new strategies for achieving these principles, and as a result some traditional measures of success are no longer valid. We classified and ranked the performance of twenty business-to-consumer eCommerce companies by developing critical benchmarks using the Balanced scorecard methodology. We applied a Latent class model, a statistical model along the Bayesian framework, to facilitate the determination of the best and worst performing companies. An eCommerce site's greatest asset is its customers, which is why some of the most valued and sophisticated metrics used today evolve around customer behavior. The results from our classification and ranking procedure showed that companies that ranked high overall also ranked comparatively well in the customer analysis ranking, For example, Amazon.com, one of the highest rated eCommerce companies with a large customer base ranked second in the critical benchmark developed towards measuring customer analysis. The results from our simulation also showed that the Latent class model is a good fit for the classification procedure, and it has a high classification rate for the worst and best performing companies. The resulting work offers a practical tool with the ability to identify profitable investment opportunities for financial managers and analysts.

Choice-Based Conjoint (CBC) models are often used for pricing decisions, especially when scanner data models cannot be applied. Up to date, it is unclear how Choice-Based Conjoint (CBC) models perform in terms of forecasting real-world shop data. In this contribution, we measure the performance of a Latent Class CBC model not by means of an experimental hold-out sample but via aggregate scanner data. We find that the CBC model does not accurately predict real-world market shares, thus leading to wrong pricing decisions. In order to improve its forecasting performance, we propose a correction scheme based on scanner data. Our empirical analysis shows that the hybrid method improves the performance measures considerably. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Developing sound or reliable statistical models for analyzing vehicle crashes is very important in highway safety studies. A difficulty arises when crash data exhibit overdispersion. Over-dispersion caused by unobserved heterogeneity is a serious problem and has been addressed in a variety ways within the negative binomial (NB) modeling framework. However, the true factors that affect heterogeneity are often unknown to researchers, and failure to accommodate such heterogeneity in the model can undermine the validity of the empirical results. Given the limitations of the NB regression model for addressing over-dispersion of crash data due to heterogeneity, this research examined an alternative model formulation that could be used for capturing heterogeneity through the use of finite mixture regression models. A Finite mixture of Poisson or NB regression models is especially useful when the count data were generated from a heterogeneous population. To evaluate these models, Poisson and NB mixture models were estimated using both simulated and empirical crash datasets, and the results were compared to those from a single NB regression model. For model parameter estimation, a Bayesian approach was adopted, since it provides much richer inference than the maximum likelihood approach. Using simulated datasets, it was shown that the single NB model is biased if the underlying cause of heterogeneity is due to the existence of multiple counting processes. The implications could be poor prediction performance and poor interpretation. Using two empirical datasets, the results demonstrated that a two-component finite mixture of NB regression models (FMNB-2) was quite enough to characterize the uncertainty about the crash occurrence, and it provided more opportunities for interpretation of the dataset which are not available from the standard NB model. Based on the models from the empirical dataset (i.e., FMNB-2 and NB models), their relative performances were also examined in terms of hotspot identification and accident modification factors. Finally, using a simulation study, bias properties of the posterior summary statistics for dispersion parameters in FMNB-2 model were characterized, and the guidelines on the choice of priors and the summary statistics to use were presented for different sample sizes and sample-mean values.

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The Virginia Guidelines for Uniform Performance Standards and Evaluation Criteria for Teachers represented a significant overhaul of conventional teacher evaluation criteria in Virginia. The policy outlined seven performance standards by which all Virginia teachers would be evaluated. This study explored the application of cognitive diagnostic modeling to measure teachers' perceptions of intra-organizational mechanisms available to support educational professionals in implementing this policy. It was found that a coarse-grained, four-attribute compensatory, re-parameterized unified model (C-RUM) fit teacher perception data better and had lower standard errors than the competing finer-grained models. The Q-matrix accounted for the complex loadings of items to the four theoretically and empirically driven mechanisms of implementation support including characteristics of the policy, teachers, leadership, and the organization. The mechanisms were positively, significantly, and moderately correlated which suggested that each mechanism captured a different, yet related, component of policy implementation support. The diagnostic profile estimates indicated that the majority of teachers perceived support on items relating to "characteristics of teachers." Moreover, almost 60% of teachers were estimated to belong to profiles with perceived support on "characteristics of the policy." Finally, multiple group multinomial log-linear models (Xu and Von Davier, 2008) were used to analyze the data across subjects, grade levels, and career status. There was lower perceived support by STEM teachers than non-STEM teachers who have the same profile, suggesting that STEM teachers required differential support than non-STEM teachers. The precise diagnostic feedback on the implementation process provided by this application of diagnostic models will be beneficial to policy makers and educational leaders. Specifically, they will be better prepared to identify strengths and weaknesses and target resources for a more efficient, and potentially more effective, policy implementation process. It is assumed that when equipped with more precise diagnostic feedback, policy makers and school leaders may be able to more confidently engage in empirical decision making, especially in regards to targeting resources for short-term and long-term organizational goals subsumed within the policy implementation initiative. / Ph. D.

Nepakankamos informacinių sistemų projektavimo priemonių galimybės labai išplečia projekta-vimo proceso trukmę. Dažnai projektavimo ciklas susideda iš kelių etapų. Paprastai pradžioje aprašoma probleminė sritis (kaupiamos žinios apie veiklą), specifikuojami vartotojo reikalavimai, po to sudarinėjami projektiniai sprendimai (sistemos klasių modelis ir kt.). Projektinių sprendimų kūrimas veiklos žinių pagrindu nėra automatizuotas, dėl to šis procesas užtrunka ilgai. Neautomatizuotas IS projektavimas ne tik užtrunka ilgiau, bet ir įveda didesnę klaidos pasitai-kymo galimybę. T.y. žmogiškas faktorius gali lemti, kad bus atsižvelgta ne į visus veiklos žinių bazėje saugomus įrašus. Įrašai taip pat gali būti ne tinkamai interpretuojami ir t.t. Darbe plačiai analizuojamos IS kūrimo priemonės ir atsižvelgiant į privalumus ir trūkumus pasiūlomas metodas proceso optimizavimui. Metodas - skirtas klasių modelio sudarymui pagal veiklos žinias. / Insufficient capabilities of information systems design tools extends the duration of the design process. Often, the design cycle consists of several stages. Usually at the beginning, then the domain is described (collected knowledge of the activities), analytics specifies user requirements. Only after this design solution (the system class models, etc.) can be developed. Enterprise Knowledge-based Devel-opment of the design solutions is not automated therefore the duration of the design process is long. There are a lot of disadvantages of not automated process. Non-automated design not only takes longer, but also creates a greater possibility of the error occurrence. That is because the human factor can be taken into account because, that not all the activities stored in the knowledge base. Also records may not be correctly interpreted, and etc. Thus, the expected solution is a method which will be used to generate information system de-sign solutions (specifically - information system class model of the platform independent level) based on the knowledge (the description of the domain) stored in knowledge base. Of all design solutions, the class model is one of the major information system project solutions. Therefore this should efficiently optimize the design process of information system. In this paper you will discover the possible solution – proposed prototype of algorithm and pro-totype of knowledge base. Solution is realized on “MagicDraw UML” platform therefore it... [to full text]

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