System dynamics can be a very useful tool to expand the boundaries of one's mental models to better understand the underlying behavior of systems. But despite its utility, there remains challenges associated with system dynamics modeling that the current research addresses by expanding the system dynamics modeling toolbox. The first challenge relates to imprecision or vagueness, for example, with respect to human perception and linguistic variables. The most common approach is to use table or graph functions to capture the inherent vagueness in these linguistic (qualitative) variables. Yet, combining two or more table functions may lead to further complexity and, moreover, increased difficulty when analyzing the resulting behavior. As part of this research, we extend the system dynamics toolbox by applying fuzzy logic. Then, we select a problem of congestion pricing in mitigating traffic congestion to verify the effectiveness of our integration of fuzzy logic into system dynamics modeling. Another challenge, in system dynamics modeling, is defining proper equations to predict variables based on numerous studies. In particular, we focus on published equations in models for energy balance and weight change of individuals. For these models there is a need to define a single robust prediction equation for Basal Metabolic Rate (BMR), which is an element of the energy expenditure of the body. In our approach, we perform an extensive literature review to explore the relationship between BMR and different factors including age, body composition, gender, and ethnicity. We find that there are many equations used to estimate BMR, especially for different demographic groups. Further, we find that these equations use different independent variables and, in a few cases, generate inconsistent conclusions. It follows then that selecting a single equation for BMI can be quite difficult for purposes of modeling in a systems dynamics context. Our approach involves conducting a meta-regression to summarize the available prediction equations and identifying the most appropriate model for predicting BMR for different sub-populations. The results of this research potentially could lead to more precise predictions of body weight and enhanced policy interventions to help mitigate serious health issues such as obesity. / Ph. D.

A Quantitative Microbial Risk Assessment (QMRA) has been undertaken to utilize research on Shiga-toxin Escherichia coli (STEC) contamination in beef for the benefit of public health. The QMRA operates as a 2nd order Monte Carlo simulation to create stochastic mathematical models that incorporate all of the key components of STEC contamination from farm to fork. The resulting model is able to identify knowledge gaps, public health risks, and simulate theoretical changes in the beef system. However, high variability in processing plant intervention literature has prompted a meta-analysis to determine informed estimates of intervention effectiveness for QMRA parameterization. Meta-analysis derived least-squares means bacterial log reductions for acetic acid, lactic acid, steam vacuum, and water wash interventions on carcass surfaces (n=249) were 1.44 [95% CI: 0.73 – 2.15], 2.07 [1.48 – 2.65], 3.09 [2.46 – 3.73], and 1.90 [1.33 – 2.47] log CFU/cm2, respectively. Least-squares means log reductions for acetic acid, lactic acid, sodium hydroxide, and water wash on hide surfaces (n=47) were 2.21 [1.36 – 3.05], 3.02 [2.16 – 3.88], 3.66 [2.60 – 4.72], and 0.08 [-0.94 – 1.11] log CFU/cm2, respectively. Meta-regressions showed that temperature, duration of application, microbial starting concentration, extra water washes, inoculation type, organism type, sample method, surface type, and antimicrobial concentrations were all significant predictors of intervention effectiveness. Finally, after observing authors use substituted values for samples found below a detection limit in primary plant intervention literature, simulations were run to assess the impact of substitution on a random-effects meta-analysis. Simulation results show that substitution practices artificially decrease effectiveness estimates and increase heterogeneity. / Master of Science

Motor vehicle crashes are one of the leading causes of death in the United States. Traffic safety research targets at understanding the cause of crash, preventing the crash, and mitigating crash severity. This dissertation focuses on the driver-related traffic safety issues, in particular, on developing and implementing contemporary statistical modeling techniques on driving risk research on Naturalistic Driving Study data. The dissertation includes 5 chapters. In Chapter 1, I introduced the backgrounds of traffic safety research and naturalistic driving study. In Chapter 2, the state-of-practice statistical methods were implemented on individual driver risk assessment using NDS data. The study showed that critical-incident events and driver demographic characteristics can serve as good predictors for identifying risky drivers. In Chapter 3, I developed and evaluated a novel Bayesian random exposure method for Poisson regression models to account for situations where the exposure information needs to be estimated. Simulation studies and real data analysis on Cellphone Pilot Analysis study data showed that, random exposure models have significantly better model fitting performances and higher parameter coverage probabilities as compared to traditional fixed exposure models. The advantage is more apparent when the values of Poisson regression coefficients are large. In Chapter 4, I performed comprehensive simulation-based performance analyses to investigate the type-I error, power and coverage probabilities on summary effect size in classical meta-analysis models. The results shed some light for reference on the prospective and retrospective performance analysis in meta-analysis research. In Chapter 5, I implemented classical- and Bayesian-approach multi-group hierarchical models on 100-Car data. Simulation-based retrospective performance analyses were used to investigate the powers and parameter coverage probabilities among different hierarchical models. The results showed that under fixed-effects model context, complex secondary tasks are associated with higher driving risk. / Ph. D.

<p dir="ltr">The purpose of this three-paper dissertation is to examine the effect of the Community of Inquiry (CoI) framework-informed use of social media on language learning through a meta-analysis and two mixed-method studies. The first paper is a meta-analysis that examines the overall effect of social media use on language learning and the factors that moderate the effect. The second paper is a mixed-method study to investigate the effect of the Community of Inquiry-informed use of Facebook discussion activities on English speaking. The third paper is a mixed-method study to examine the effect of the Community of Inquiry-informed use of Facebook discussion activities on English writing. The combined results expanded our understanding of the overall effect of the use of social media on language learning and the applicability of the CoI framework in social media platforms. Finally, the last chapter synthesized the findings across three papers and presented implications for research and practice. </p><p><br></p>

Yes / The aim of this research is to synthesise findings from existing studies on the characteristics of source credibility of electronic word of mouth (eWOM) communications in a single model by using meta-analysis. Findings from 20 research papers show that source expertise, trustworthiness, and homophily significantly influence perceived eWOM usefulness and credibility, intention to purchase, and information adoption. The results of this study add to existing knowledge of the influence of source characteristics on consumer behaviour, which will advance our understanding of information processing. Marketers can use the findings of this meta-analysis to enhance their marketing activities.

Yes / Based on a critical review of the Unified Theory of Acceptance and Use of Technology (UTAUT), this study first formalized an alternative theoretical model for explaining the acceptance and use of information system (IS) and information technology (IT) innovations. The revised theoretical model was then empirically examined using a combination of meta-analysis and structural equation modelling (MASEM) techniques. The meta-analysis was based on 1600 observations on 21 relationships coded from 162 prior studies on IS/IT acceptance and use. The SEM analysis showed that attitude: was central to behavioural intentions and usage behaviours, partially mediated the effects of exogenous constructs on behavioural intentions, and had a direct influence on usage behaviours. A number of implications for theory and practice are derived based on the findings.

Yes / ‘Habit’ was the most important theoretical addition into UTAUT2 to challenge the role of behavioural intention as a lone predictor of technology use. However, systematic review and meta-analysis of Price value the other UTAUT2 additional construct revealed major inconsistency of the model with just 41% UTAUT2 based studies including the construct in their research. Thus, the aim of this research is to understand the appropriateness of ‘habit’ construct usage among UTAUT2 based empirical studies and their reason for omission or inclusion. The findings from 66 empirical studies revealed only 23 studies a meagre (35%) utilised ‘habit’ construct and the remaining massive 43 studies (65%) excluded the construct from their research model. The major reason for studies not including “habit” construct was they were examining users of new technology at early stage of adoption where sufficient time hasn’t elapsed for users to form habit. Moreover this study caution the use of experience as an alternative for habit. Since experience can be gained under mandatory settings which is not sufficient enough to form habit that occurs more naturally under voluntary settings. This study also provided number of recommendations for theory and practice based on the findings.

Yes / In the broad sphere of Operations Management, Supply Chain Risk Management (SCRM) is a significant area of interest for both academics and practitioners. As SCRM has transitioned from an emerging topic to a growing research area, there is a need to review existing literature in order to ascertain development in this area. There are many literature reviews on this topic, however, there is a lack of an extensive review using network analysis and meta-analysis within SCRM context including ripple effect. To address this gap, we performed a review of 2564 articles published in peer-reviewed academic journals from 1976 to December 2018. First, we apply a network analysis tool on 2564 articles and identify emerging research clusters. Second, to conduct meta-analysis, we collated empirical results from the studies identified. Of those 2564 articles, 42 studies were empirical in nature including 29 studies that used a range of different constructs with appropriate correlation values required for performing meta-analysis. Through this study, we contribute to the literature on SCRM by discussing the challenges of current research, but more importantly, by identifying and proposing five research clusters and future research directions. Finally, the paper acknowledges the theoretical contribution, the limitations of this study, and suggests further research directions.

Yes / We study the role of the strength of Intellectual Property Rights (IPR) law protection and enforcement in influencing horizontal productivity spillovers from inward FDI to domestic firms in host countries. While most WTO countries adopted strong IPR legislation due to exogenous pressure resulting from the signing of the Trade-Related Aspects of IPR (TRIPS) agreement, public IPR enforcement strength continues to vary significantly between countries. We meta-analyse 49 studies and find that public IPR enforcement strength has a direct positive effect on horizontal productivity spillovers from inward FDI to domestic firms and a negative moderating effect on the relationship between IPR law protection strength and horizontal productivity spillovers from inward FDI to domestic firms.

Yes / Background: Reasoned action approach (RAA) includes subcomponents of attitude (experiential/instrumental), perceived norm (injunctive/descriptive), and perceived behavioral control (capacity/autonomy) to predict intention and behavior. Purpose: To provide a meta-analysis of the RAA for health behaviors focusing on comparing the pairs of RAA subcomponents and differences between health protection and health-risk behaviors. Methods: The present research reports a meta-analysis of correlational tests of RAA subcomponents, examination of moderators, and combined effects of subcomponents on intention and behavior. Regressions were used to predict intention and behavior based on data from studies measuring all variables. Results: Capacity and experiential attitude had large, and other constructs had small-medium-sized correlations with intention; all constructs except autonomy were significant independent predictors of intention in regressions. Intention, capacity, and experiential attitude had medium-large, and other constructs had small-medium-sized correlations with behavior; intention, capacity, experiential attitude, and descriptive norm were significant independent predictors of behavior in regressions. Conclusions: The RAA subcomponents have utility in predicting and understanding health behaviors.