Many environmental behavior models have been proposed to predict or explain environmental behavior in the past two decades. Due to limitations in practice, these models were mostly tested on a basis of self-reported measures of environmental behavior. How would these models perform if an observed measure of environmental behavior was used? What are the differences between the results of testing a model with a self-reported environmental behavior (SB) measure and the same model with an other-reported environmental behavior (OB) measure? And, what do these differences imply? A major model, Ajzen¡¦s (1985) theory of planned behavior (TPB), was tested with SB and OB measures respectively with structural equation modeling (SEM) analysis. Data were collected through questionnaires and participant observation in which students living in dormitories in a university were trained to observe their 172 roommates¡¦ five environmental behaviors. The other-reports of these roommates¡¦ environmental behavior made by the observers were based on their observation as well as experiences in living with and understanding of their roommates. Results showed that there existed significant frequency gaps between behavioral intention (BI) and SB and between SB and OB. The difference between SB and OB frequency was about 1.25 points on a 7-point scale. The BI-SB correlation (.87) was much larger than BI-OB correlation (.31) implying that SB was better predicted than OB was by BI. It was found that the subjects¡¦ SB tended to be conservative compared with BI and that OB was provided even more conservatively by the observers. BI could be the common cause of both SB and OB or SB could intervene in the relationship between BI and OB. A few significant differences were found in the results of testing the TPB model with SB and OB. The preference for the behavior might have played an essential role in predicting one¡¦s SB. The model had a very large proportion of variance explained (.814) because of the strong connection between BI and SB. It also seemed to have a better model fit when SB was used while the model¡¦s capability in predicting behavior was greatly reduced due to the large disparity between BI and OB when OB was used. Finally, it seemed that social desirability was a not cause of the SB-OB gap.

Advertising, brand penetration and retailer service have been playing important roles in marketing for a long time. There are many studies which discuss about the effects of advertising on brand penetration, the effects of advertising on perceived quality, and the effects of brand penetration on perceived quality. However, the relationship among the advertising, brand penetration and perceived quality has rarely been studied. Furthermore, the studies about the effects of retailer service on perceived quality are much fewer. Thus, the main purpose of this study is to examine the effect of brand penetration and retailer service on perceived quality. The experiment with 2¡Ñ2¡Ñ2 factorial design is conducted to collect the empirical data. In the factorial design, we have 217 respondents in the 8 cells. The experiment variables have the effects of advertising, brand penetration and retailer service on perceived quality. The respondents are the more than the junior students of Sun Yat-sen University and National Taiwan University, and the product of this study is ¡§Notebook¡¨. This study applied structural equation model (SEM) to verify. The main results of this study are as follow: (1) Advertising positively influences perceived quality and that brand penetration partially mediates this influence. (2) Advertising positively influences perceived quality and that retailer service partially mediates this influence. The findings of this study can help the chief marketing officer to make strategic decision to increase the effect of advertising on perceived quality effectively.

Nowadays experience marketing is the most common strategy to promote. Cultural- based activities in Kaohsiung is one of the examples to use such strategy. Culture is far more powerful than economy and politics. The influence by art and culture activities is direct, indirect and derivative on regional economy. Promoting art and culture activities as well as tourism industries significantly enhances conglomerate economy strength in the city. In terms of criticizing culture, the study concerns what aesthetic type created by urban experiential marketing strategy as well as what internet communications influence on cultural marketing. The study theory is based on B. Joseph Pine¢º& James H. Gilmore¡¦s experiential marketing aesthetics in 1998 and Schmitt¡¦s experience marketing theory in 1999, and thus explores three dimensions as experiential marketing strategy, aesthetics culture index and communication results. The survey research was performed by a questionnaire through convenient sampling method. A total of 280 questionnaires were administered, and 263 questionnaires were retrieved. AMOS Statistical Software was used to apply data analysis of the correlation among variables by using Structure Equation Modeling (SEM). The research results showed that: (1) experiential marketing strategy has positive effect on enhancing aesthetics culture; (2) there is no mediating effect about computer-mediated communication (CMC) between experiential marketing strategy and enhancing aesthetics culture of urban culture; (3) the cultural activities of experiential marketing strategy are prone to the entertainment. In addition to the entertainment, the long-term strategy of the government shall emphasize both on artistic and educative function when promoting the experiential marketing on urban culture. On the aspect of information context, both richness and interactivity of computer-mediated communication media content shall be enhanced. From the perspective of esthetical form of experiential marketing, we expect the findings of this study are helpful to consider the cultural information into the field of communication research in future planning for the urban cultural marketing to advance a city's global competitiveness.

Urban transport systems are facing seemingly irreconcilable problems.Sustainable transport policies are necessary to address this sustainability challenge. However,their effectiveness highly depends on the public attitude towards them. A method combiningboth qualitative and quantitative research was used to redefine a sustainable transport policybased on sustainability principles, and find out the key factors of public attitude towardssustainable transport policies. Furthermore, the interrelations between these factors arerevealed by using structural equation modelling. 1685 questionnaires were sent to fourrepresentative cities in Sweden: Stockholm, Göteborg, Uppsala and Karlskrona. By using thisfirst hand data, we discovered some differences between Karlskrona and these other threecities. The negative attitudes towards reducing car use and promoting public transport inKarlskrona is opposite to the positive attitudes in other cities. The acceptance of taxes, speedlimitation and parking regulation is notably different as well. The results also indicate that citycharacteristics influence public attitudes towards sustainable transport policies more thancitizens’ characteristics. The functionality of a city is the most significant factor. Moreover,the results show a high dependence on individual car use. This suggests that planners shouldnot use the experience gained from other cities without investigating actual local conditions.

Authentic leadership is a new concept that is gaining both popularity and notoriety in the leadership literature. It is argued as a positive form of leadership that goes beyond traditional leadership styles in order to influence followers through genuine, ethical behavior. However, as a concept in its infancy, authentic leadership has yet to receive much empirical attention, and many researchers are skeptical of its value in what is seen as a saturated domain of leadership styles. This study offers a comprehensive approach to addressing this need. A new measure for authentic leadership was developed and validated through pilot testing. Through additional analyses using this new measure, it was discovered that authentic and transformational leadership were not empirically distinct. However, by combining these two measures into an authentic-transformational leadership construct, it was still possible to examine the effect of greater amounts of authenticity in the leadership role. It was found that authentic-transformational leadership was directly related to a number of employee attitudes, and these, in turn, were related to positive employee behaviors. Results of this study are discussed both in terms of future research in the area of authentic-transformational leadership as well as its impact on organizational effectiveness.

Information systems literature calls for websites providing a site architecture that is as close as possible to the mental model of the user. To assure that users will be satisfied, website designers need to be aware that users interpret elements offered on a website based on their physiological and psychological factors. Creating a compelling online experience for diverse groups of e-customers is a challenge and of utmost importance for a website's success. However, in the field of tourism literature that discriminates between requirements based on motivational or cognitive aspects of certain user-groups is scarce. Therefore, the hypothesis examined in this dissertation is that there are differences between a priori defined user-groups regarding their satisfaction with web 2.0 websites. Further, this research is aimed at not only taking the demand side into account but also the supply side by asking whether the supply side is aware of the increasing importance of web 2.0 contents and its potentials for information presentation and market research. To bring the project into a coherent framework three more aspects are tackled. First, due to the fact that there is no existing typology for travel-blogs this dissertation tries to fill this gap by means of a qualitative approach. Second, an alternative, more parsimonious measurement approach for website performance is proposed. Finally, a study on measuring emotional mental models, a topic which seems to be neglected in information systems literature as well as in the field of tourism, is included. In order to investigate all these issues nine empirical studies are conducted. The approaches used include online surveys, content analysis, and quasi-experimental design. For data analysis methods such as Partial Least Squares Structural Equation Modeling, Covariance Based Structural Equation Modeling, and Artificial Neural Network Analysis are applied. The results indicate that there are differences between a priori defined user groups regarding their satisfaction with web 2.0 websites. For instance, the influence of motivational factors on the importance of website features differs between travelers who seek relaxation and those who seek adventure. Moreover, the main drivers for value and satisfaction for a goal-directed search are content quality followed by usefulness. These effects are attenuated for the experiential search. The study focusing on communication modes suggests that for verbalizers content is most essential while for visualizers the most important aspect is design. A further study, investigating the influence of hotel guest reviews on customer hotel preferences, reveals that in certain instances the subjects' willingness to pay is significantly higher than their reference price. Additionally, the findings indicate that people who read consumer reviews online do not belong to only one homogeneous group but perceive the importance of review categories differently. Pertaining to the supply side findings show that managers in Austria, Germany, and Switzerland assess user generated content as highly important and that managers have a rather positive attitude towards negative reviews. However, there is evidence that hotels and museums are not seizing opportunities the Internet provides in terms of information presentation. Concerning the formative measurement developed for website performance cross validation proved that the index works well. The last study sheds light on measuring emotional models and gives insights into changes of users' emotional mental models before and after their visitation of a virtual world. The final chapter "Discussion and conclusions" critically discusses the results of the nine studies from a theoretical point of view, debates the approaches used and methods applied, and gives managerial implications and suggestions for future research. (author's abstract)

This study examined the interrelationships between adolescent weight and several other variables thought to impact weight and obesity: physical activity, depressive symptoms, binge-eating symptoms, dieting, socio-economic status, special education status, gender, and ethnicity. The sample consisted of 251 high school students in rural Florida who completed measures of depression, binge-eating, dieting, and physical activity. Measurement instruments included the Reynolds Adolescent Depression Scale-2nd Edition (RADS-2), the bulimia scale of the Eating Disorder Inventory-2 (EDI-2), the dieting scale of the Dutch Eating Behavior Questionnaire (DEBQ), and a physical activity questionnaire derived from the Youth Risk Behavior Scale for Students (YRBSS). The study utilized path analysis, a group correlational design, to determine whether the proposed path model fit the data. Obese and non-obese students also were compared with regard to a) the levels of binge-eating symptoms, and b) the relationship between binge-eating and depression. Path analysis results were not statistically or clinically significant, suggesting a poor fit of the model to the data. Results indicated 19% of participants were obese and 20% were overweight. More than three times as many obese students than non-obese students reported experiencing a binge-eating experience at some time in the past. However, on the bulimia scale of the EDI-2, obese and non-obese participants did not differ statistically in their responses. Statistically but not clinically significant correlations were revealed between depression and binge-eating for the sample and also for non-obese students. For the sub-sample of 13 students who had both binged and dieted, 7 had binged first, 3 had dieted first, and 3 binged and dieted for the first time at the same age. Future research should continue to investigate the relationships of the variables related to obesity. Future directions might include a larger sampl e size and a modified sample selection process. Action research should continue in the areas of obesity prevention and intervention, and student services personnel should promote healthy lifestyle choices and a recognition of obesity as a socio-cultural problem.

Despite considerable advances in missing data imputation methods over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions. These techniques can be categorised into two classes: statistical methods of data imputation and computational intelligence methods of data imputation. Due to the longstanding use of statistical methods in handling missing data problems, it takes quite some time for computational intelligence methods to gain profound attention even though these methods have analogous accuracy, in comparison to other approaches. The merits of both these classes have been discussed at length in the literature, but only limited studies make significant comparison to these classes. This thesis contributes to knowledge by firstly, conducting a comprehensive comparison of standard statistical methods of data imputation, namely, mean substitution (MS), regression imputation (RI), expectation maximization (EM), tree imputation (TI) and multiple imputation (MI) on missing completely at random (MCAR) data sets. Secondly, this study also compares the efficacy of these methods with a computational intelligence method of data imputation, ii namely, a neural network (NN) on missing not at random (MNAR) data sets. The significance difference in performance of the methods is presented. Thirdly, a novel procedure for handling missing data is presented. A hybrid combination of each of these statistical methods with a NN, known here as the post-processing procedure, was adopted to approximate MNAR data sets. Simulation studies for each of these imputation approaches have been conducted to assess the impact of missing values on partial least squares structural equation modelling (PLS-SEM) based on the estimated accuracy of both structural and measurement parameters. The best method to deal with particular missing data mechanisms is highly recognized. Several significant insights were deduced from the simulation results. It was figured that for the problem of MCAR by using statistical methods of data imputation, MI performs better than the other methods for all percentages of missing data. Another unique contribution is found when comparing the results before and after the NN post-processing procedure. This improvement in accuracy may be resulted from the neural network's ability to derive meaning from the imputed data set found by the statistical methods. Based on these results, the NN post-processing procedure is capable to assist MS in producing significant improvement in accuracy of the approximated values. This is a promising result, as MS is the weakest method in this study. This evidence is also informative as MS is often used as the default method available to users of PLS-SEM software.

Pollution studies have sought to understand the relationships between adverse health effects and harmful exposures. Many environmental health studies are predicated on the idea that each exposure has both acute and long term health effects that need to be accurately mapped. Considerable work has been done linking air pollution to deleterious health outcomes but the underlying biological pathways and contributing sources remain difficult to identify. There are many statistical issues that arise in the exploration of these longitudinal study designs such as understanding pathways of effects, addressing missing data, and assessing the health effects of multipollutant mixtures. To this end this dissertation aims to address the afore mentioned statistical issues. Our first contribution investigates the mechanistic pathways between air pollutants and measures of cardiac electrical instability. The methods from chapter 1 propose a path analysis that would allow for the estimation of health effects according to multiple paths using structural equation models. Our second contribution recognizes that panel studies suffer from attrition over time and the loss of data can affect the analysis. Methods from Chapter 2 extend current regression calibration approaches by imputing missing data through the use of moving averages and assumed correlation structures. Our last contribution explores the use of factor analysis and two-stage hierarchical regression which are two commonly used approaches in the analysis of multipollutant mixtures. The methods from Chapter 3 attempt to compare the performance of these two existing methodologies for estimating health effects from multipollutant sources.

The present research reports the first comprehensive map of brain networks underlying latent inhibition learning, the first application of structural equation modeling to cytochrome oxidase data, and the first effects of methylene blue, a known metabolic enhancer, on latent inhibition. In latent inhibition, repeated exposure to a stimulus results in a latent form of learning that inhibits subsequent associations with that stimulus. As neuronal energy demand to form learned associations changes, so does the induction of the respiratory enzyme cytochrome oxidase. Therefore, cytochrome oxidase can be used as an endpoint metabolic marker of the effects of experience on regional brain metabolic capacity. Quantitative cytochrome oxidase histochemistry was used to map brain regions in mice trained on a tone-footshock fear conditioning paradigm with either tone preexposure (latent inhibition), conditioning only (acquisition), conditioning followed by tone alone (extinction), or no handling or conditioning (naïve). In normal latent inhibition, the ventral cochlear nucleus, medial geniculate, CA1 hippocampus, and perirhinal cortex showed modified metabolic capacity due to latent inhibition. Structural equation modeling was used to determine the causal influences in an anatomical network of these regions and others thought to mediate latent inhibition, including the accumbens and entorhinal cortex. An uncoupling of ascending influences between auditory regions was observed in latent inhibition. There was also a reduced influence on the accumbens from the perirhinal cortex in both latent inhibition and extinction. These results suggest a specific network with a neural mechanism of latent inhibition that involves sensory gating, as evidenced by modifications in metabolic capacity, effective connectivity between auditory regions, and reduced hippocampal influence on the accumbens. The effects of methylene blue on disrupted latent inhibition were also investigated. Reduced tone-alone presentations disrupted the latent inhibition effect and led to an increase in freezing behavior. Repeated low-dose administration of methylene blue decreased freezing levels and facilitated the disrupted latent inhibition effect. Methylene blue administration also resulted in changes in metabolic capacity in limbic and cortical regions. A unique functional neural network was found in methylene blue-restored latent inhibition that emphasized sensory gating of auditory information, attention processing, and cortical inhibition of behavior. / text