The present experiment investigated the application of Green and Swets (1966) signal-detection theory to undesirable behavior as a method of reducing unwanted behaviors using reinforcement and extinction. This experiment investigated the use of this stimulus control technique to reduce undesirable behaviors using a multiple-baseline design. Once the cue for a target behavior was established and maintained, the use of the verbal cue was reduced in frequency and the rate of unprompted undesirable behavior was recorded. Generalization was tested across multiple people. Data for this experiment showed that undesirable behavior could be reduced by altering the stimulus control that maintained it.

Arklių nenormalaus elgesio priežasčių analizė. Baigiamojo darbo apimtis 48 lapai, iš viso 24 lentelės ir 13 paveikslų, naudota 30 literatūros šaltinių. Baigiamojo darbo tikslas - išanalizuoti nepageidaujamą arklių elgesį sukeliančias priežastis. Tyrimo metu buvo ištirti 25 arkliai, kurie rodė nepageidaujamą elgesį. Visi atvejai buvo suskirstyti į tris grupes: oralinę stereotipinę elgseną rodantys, motorinę stereotipinę elgseną rodantys ir kitą nepageidautiną elgseną rodantys arkliai. Išsiaiškintos tirtų arklių laikymo sąlygos, įvertinta jų gyvenamoji aplinka, šėrimas, sveikatingumas, paimti ir ištirti kraujo ir išmatų mėginiai. Tyrimo metu nustatyta, kad nepageidaujamai elgsenai susiformuoti įtakos turi gyvenamosios aplinkos stimuliacija, netinkamas šėrimas, nepakankamas mocionas ir socialinis kontaktas bei parazitų invazija. / Analysis of the causes of abnormal behaviour in horses. Masters thesis volume 48 sheets, in content 24 tables, 13 pictures, used 30 literary sourse. The aim of thesis is to analyze causes of undesirable equine behaviour. In study was investigated 25 horses, who showed signs of undesirable behaviour. All cases was divided into three groups: oral stereotypic behaviour, locomotor stereotypic behaviour and other undesirable behaviur. The housekeeping conditions, feeding, healthy was investigated. Also kept samples of blood and feaces. The study showed that undesirable horse behaviour develops of living environment stimuli, unbalanced feeding, poor movement and social environment and also parasites invasion.

This thesis examines banking efficiency and the productivity of thirteen transition Central and Eastern European banking systems during 1998-2003 using Data Envelopment Analysis (DEA). It proposes a non-parametric methodology for non-radial Russell output efficiency measure of banking firms, incorporating risk as an undesirable output. In addition, the proposed efficiency measure handles unrestricted data, i. e. both positive and negative. The Luenberger productivity index is suggested, which is applicable to technology where the desirable and undesirable outputs are jointly produced, and are possibly negative. Furthermore, the thesis addresses the main issue in the literature on banking performance measurement, which concerns the lack of consistency in the conceptual and theoretical considerations in describing the banking production process. Consequently, a metaanalysis tool, to examine the choice of inputs and outputs definitions in the banking efficiency literature, is suggested. In addition, the performance measures are estimated using three alternative definitions of the banking production process focusing on the risk and environmental dimensions of bank efficiency and productivity, with further comparative analysis using bootstrapping and kernel density techniques. Overall, the empirical results suggest that in Central and Eastern Europe Czech, Hungarian and Polish banks were the most technical efficient banks and the banking risk was mainly affected by external environmental factors during the analyzed period. Productivity analysis implies that the main driver of productivity change in the Central and Eastern European banks is the technological improvement. As meta-analysis revealed, the choice of particular approach of describing the banking production process is determined not by the availability of particular input or output variable information but the concepts of researcher's theoretical considerations. Statistical tests and density analysis indicate that efficiency scores, returns parameters and productivity indexes are sensitive to the choice of particular approaches.

Approximately 1.8% of students in the public school system have an intellectual disability or Autism Spectrum Disorder. These disabilities cause impairment in multiple domains of functioning. If these students also have challenging behaviors, such as noncompliance, aggression, and stereotypies, these behaviors have been found to cause impairment over and beyond those of the core symptoms associated with the disability. Challenging behaviors in youth with developmental disabilities do not typically subside on their own and need intervention. Thankfully, there are evidence-based behavioral interventions for individuals with developmental disabilities to reduce challenging behaviors and increase more functional behaviors including Applied Behavioral Analysis, Functional Behavioral Analysis, and School-Wide Positive Behavioral Support and Interventions (SWPBIS). There has been much research and positive effects found on the effectiveness of behavioral interventions for individuals with developmental disabilities, and there have been numerous meta-analyses conducted to synthesize these results. However, there have been only a few meta-analyses examining the effectiveness of school-based behavioral interventions for youth with developmental disabilities. A gap in the literature exists in understanding the effectiveness of behavioral interventions in schools from a SWPBIS perspective for youth with developmental disabilities. There also is a need to examine a wider range of dates and to examine the use of parametric statistical metrics. The current study addressed these issues by conducting a meta-analysis of single-case design studies over approximately the past 20 years to add to the current understanding of the effect of school-based behavioral interventions on behavioral outcomes of youth with developmental disabilities. Additionally, moderator analyses were conducted on numerous participant, intervention, and study characteristics that have been deemed important in the literature. The effect size of behavioral interventions on youths’ behavioral outcomes was determined through the use of a parametric statistical method, hierarchical linear modeling. The effect size was found to be large for a single case design synthesis of 3.31 and there were two moderating effects located, one being the type of classroom a participant was educated in and the other the type of specific outcome studied. The current study is important for decision makers in schools in terms of deciding on the specifics of behavioral interventions for youth with an intellectual disability. Additionally, the results of the study may be pertinent to other practitioners who work with youth is schools and their caregivers so that they can utilize school-based interventions to help increase the presentation of appropriate behaviors and reduction of challenging behaviors.

Social capital resources for the firm can be conceptualized as those executive-to-executive connections held by a firm’s top management team, as well as firm-to-firm relationships that exist fairly independently of particular individuals. This type of resource can compose an important portion of any firm’s overall resource portfolio. The potential benefits associated with social capital include enhanced economic exchange opportunities, improved innovation capabilities and increased firm survival rates, among others. This study adds to the literature stream focusing on the positive consequences of social capital by demonstrating the cross-level influence of social capital on the development of reciprocity within a joint venture network. It also highlights the link between social capital resources and the quality of knowledge available to a firm via its joint venture partnerships. More importantly, though, we specifically investigate the conditions under which a firm’s social capital (firm-to-firm relationships or the social capital held by key executives) can contribute to undesirable firm-level behaviors. One often mentioned, yet rarely explored dimension of social capital is the phenomenon frequently called the ‘dark side’ of social capital. This dark side of social capital is argued to exist whenever the behavioral expectations accompanying social capital limit contribute to undesirable outcomes for the firm. Several hypotheses are tested in the context of joint ventures among S&P 500 firms. The likelihood of a firm having legal action taken against it by federal regulatory agencies or other firms is demonstrated herein to be related to the number and strength of social capital relationships. In general this research supports the view that having a large number of weak ties is beneficial for firms. More specifically, we found that in the wake of the passage of the Sarbanes-Oxley Act of 2002, an inverse relationship exists between the likelihood of firms engaging in the undesirable behaviors investigated and the number of Boards of Directors on which the firms’ respective executives held seats. Conversely, firms were more likely to engage in these undesirable behaviors whenever the firm-to-firm ties within their network of joint ventures were strongest. Furthermore, executive discretion was highly related to the likelihood of firms engaging in undesirable behaviors.

Social capital resources for the firm can be conceptualized as those executive-to-executive connections held by a firm’s top management team, as well as firm-to-firm relationships that exist fairly independently of particular individuals. This type of resource can compose an important portion of any firm’s overall resource portfolio. The potential benefits associated with social capital include enhanced economic exchange opportunities, improved innovation capabilities and increased firm survival rates, among others. This study adds to the literature stream focusing on the positive consequences of social capital by demonstrating the cross-level influence of social capital on the development of reciprocity within a joint venture network. It also highlights the link between social capital resources and the quality of knowledge available to a firm via its joint venture partnerships. More importantly, though, we specifically investigate the conditions under which a firm’s social capital (firm-to-firm relationships or the social capital held by key executives) can contribute to undesirable firm-level behaviors. One often mentioned, yet rarely explored dimension of social capital is the phenomenon frequently called the ‘dark side’ of social capital. This dark side of social capital is argued to exist whenever the behavioral expectations accompanying social capital limit contribute to undesirable outcomes for the firm. Several hypotheses are tested in the context of joint ventures among S&P 500 firms. The likelihood of a firm having legal action taken against it by federal regulatory agencies or other firms is demonstrated herein to be related to the number and strength of social capital relationships. In general this research supports the view that having a large number of weak ties is beneficial for firms. More specifically, we found that in the wake of the passage of the Sarbanes-Oxley Act of 2002, an inverse relationship exists between the likelihood of firms engaging in the undesirable behaviors investigated and the number of Boards of Directors on which the firms’ respective executives held seats. Conversely, firms were more likely to engage in these undesirable behaviors whenever the firm-to-firm ties within their network of joint ventures were strongest. Furthermore, executive discretion was highly related to the likelihood of firms engaging in undesirable behaviors.

In this thesis, single undesirable and semi-desirable facility location problems are analyzed in a continuous planar region considering the interaction between the facility and the existing demand points. In both problems, the distance between the facility and the demand points is measured with the rectilinear metric. The aim in the first part where the location of a pure undesirable facility is considered, is to maximize the distance of the facility from the closest demand point. In the second part, where the location of a semi-desirable facility is considered, a conflicting objective measuring the service cost of the facility is added to the problem of the first part. For the solution of the first problem, a mixed integer programming model is used. In order to increase the solution efficiency of the model, new branch and bound strategies and bounding schemes are suggested. In addition, a geometrical method is presented which is based on upper and lower bounds. For the biobjective problem, a three-phase interactive geometrical branch and bound algorithm is suggested to find the most preferred efficient solution.

Purpose - Collaborative consumption, such as car sharing, speci fi cally implicates customer-to-customer interaction, which must be regulated by service providers (companies, peers and self-regulating communities), comprising different challenges for business organizations. While in conventional business relations, consumers are protected from undesirable customer behavior by laws, regulations (power) in the context of collaborative consumption are rare, so that trust becomes more relevant. It is the purpose of the study to investigate possible mechanisms to prevent undesirable customers in collaborative consumption. Design/methodology/Approach - In between subject designs, samples of 186 and 328 consumers fi lled in experimental online questionnaires with vignettes. Analyses were made of differences among car sharing companies, private persons and car sharing communities in terms of the power of providers, trust in providers and trust in other users of the shared goods, undesirable customer behavior and consumer - provider relations. Findings - Companies, private persons and self-regulating communities differ in terms of perceived power and trust. Participants speci fi cally perceive mainly coercive power with the car sharing company, but with the private person and the community, reason-based trust in other users is perceived as prevalent. Nevertheless, undesirable customer behavior varies only marginally over the models. Originality/value - The present study is the first to investigate measures to prevent undesirable customer behavior over different collaborative consumption models. This enables appropriate identification of market segments and tailoring of services. The study identifies opportunities for companies in contrast to private persons and self-regulating communities and, in doing so, provides important stimulation for marketing strategy and theory development.

Benchmarking is a technique used by Decision Making Units (DMUs) to enable continuous quality improvement. Benchmarking includes almost any activity that compares a DMU's performance with some standard. Benchmarking offers the possibility of optimizing the DMU's processes, services, outcomes and products through those comparisons. Quite often, benchmarking is understood to be an act of imitating or copying but in reality benchmarking proves to be a concept that helps in innovation rather than imitation. Though benchmarking is not new, it has become popular both as an analytical research instrument and a practical decision-support tool. To some, benchmarking is not a choice; it is a necessity. Indeed, the penalty for neglecting proper benchmarking is loss of competitive edge, which is the key to survival and profitability.Usually, benchmarking involves four distinct phases. Phase I: determine the set of comparison partners. There are three types of benchmarking procedure: internal benchmarking (i.e. the benchmark is chosen within the same organization), functional benchmarking (i.e. the benchmark is chosen regardless of which industry they are) and competitive benchmarking (i.e a competitor is used as the benchmark). Phase II: collect the data. Much information is already in the public domain (financial reports, newspaper reports, analysts' reports) but it is unlikely to provide all the information required for a successful benchmarking exercise. Phase III: analyze the collected information which results in the creation of a model and an identification of performance gaps. The model will have huge influence on the results. It is crucial to motivate all assumptions made in that phase. The model could be specific to the benchmarking exercise. Phase IV: the action phase. Analyzing the reasons for the performance differentials and use the findings to redefine goals, redesign processes, and change expectations regarding the evaluated DMU's own functions and activities.Amongst the models chosen in Phase III, Data Envelopment Analysis (DEA) has received more and more attention in the benchmarking literature. The goal of such analysis is to evaluate the efficiency of a DMU by comparing its input-output performance to that of other DMUs operating in a similar technological environment. The increasing attention for DEA could be explained by two main reasons. On the one hand, DEA does not resort to any unverifiable parametric/functional specifications of the production technology but rather lets the data speak for themselves by reconstructing the production possibilities using the observed inputs and outputs and imposing some technology axioms (such as monotonicity, convexity, returns-to-scale). Consequently, DEA is nonparametric in nature. On the other hand, deviation from efficiency, which is measured as the distance to the reconstructed production possibilities, is very easily computed. Indeed, the computation of the efficiency measures merely requires solving simple linear programming problems.Recently, Cherchye et al (2008, 2013) argued that standard DEA models provide a black-box treatment of efficiency production behavior since they ignore the links between inputs and outputs, i.e. they implicitly assume that all the inputs produce all the outputs simultaneously. This assumption is not plausible in several applications (e.g. employees that are allocated to different productions processes, specific capital which is used to produce only one type of goods). These authors suggested a multi-output nonparametric efficiency measurement technique, based on a cost minimization condition, which uses available information on the allocation of inputs to outputs. The new methodology characterizes each output by its own production technology while accounting for interdependencies between the different output-specific technologies giving rise to scope economies. This methodology provides a more realistic modelling of the production process and has a bigger ability to detect inefficient behavior (i.e. has more discriminatory power) than standard DEA techniques.In this thesis, we extend the method suggested by Cherchye et al (2008, 2013) in several directions. Firstly, we incorporate bad outputs (in contrast to good outputs). This extension deals in a natural way with several limitations of existing DEA approaches to treat undesirable outputs. As demonstrated with our application to the electricity sector. Next, we extend the methodology to allow for output-specific returns-to-scale assumptions. This allows for a more flexible model that does not force the practitioners to choose the same returns-to-scale assumption for all the outputs (as it is the case for the standard DEA model). This simultaneous choice could be difficult to defend in several settings but it is surely the case when undesirable outputs are present in the production process, as demonstrated in our application. Next, we extend the methodology for multi-output producers by considering a dynamic context. We suggest a new productivity index which takes the form of a Malmquist Productivity Index. Finally, we also generalize the method of Cherchye et al (2008, 2013), based on a cost minimization condition, to a profit maximization condition. This establishes a novel DEA toolkit for profit efficiency assessments in situations with multiple inputs and multiple outputs. We apply this new model to the case of electricity plants. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

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