The rapid rate of increase in competition among the manufacturing industries has caused many organizations to continuously seek improvement in the quality of the products they manufacture to meet and exceed customer expectations. Organizations are under pressure to minimize the production costs to offer competitive prices for their products. The success story of Toyota Motor Company in implementing Lean Manufacturing (LM) has inspired many organizations around the world to adopt LM in order to improve their operational performance. There are, however, mixed results on the impact of LM on operational performance. Some studies have shown that its implementation increases operational performance while others have shown little to no improvement or even negative results. Institutional and contingency theories may provide insight into some of these contradictions and give a perception of why the implementation of LM has yielded different results on operational performance. The institutional theory states that organizations mimic the actions and practices of other organizations because of the pressure to remain competitive. Organizations in the developing countries also seem to have been imitating the Toyota Motor company that has been successful in implementing LM. On the other hand, the contingency theory states that corporations are organized according to external situations. Related to the contingency theory is the effect of Industry Clockspeed (IC). Some industries are transforming at a high speed while others are transforming at a low speed. The high IC industries are characterized by the quick development and release of new products, shorter development time and frequent changes in organizational structures. Low IC industries, however, manufacture products with a long life cycle, thus the products, processes and organizational structures for these industries change only after a long period. This study opines that the environment under which an organisation operates may affect the results of LM implementation process. The research was conducted in three parts and each of these parts is presented as chapters in this thesis. The first part (Chapter 4) gives a review and classifies the impact measurement models that have been used by various researchers to measure the success of implementing LM. These models can be classified as quantitative, qualitative, simulation-based and graphical measurement models. Pareto analysis is used to select the type of measurement model and Lean practices that are frequently used by researchers to develop Lean measurement models. The qualitative measurement model was preferred for evaluating the effect of implementing LM on operational performance because of its ability to use question structures that allow qualitative data collection for a rich analysis of opinion. With a proper structure, the questionnaire items can also be parsed and analyzed quantitatively with modern statistical techniques like Structural Equation Modelling. The Lean practices selected were Just In Time (JIT), Jidoka, People integration and Stability and standardization for building the model. This part concludes by developing a structural model that can be used to measure the impact of Lean implementation in industry, using Zimbabwean industry data. The second part (Chapter 5) evaluates the effect of implementing LM tools on operational performance across various industries in Zimbabwe. The major goal of this chapter was to develop an operational model (based on the lead from chapter 4) and test it in manufacturing organizations across various industries. A structured survey questionnaire was used for the collection of data in identified companies and 214 useful responses were obtained. The results of the study indicated that operational performance was improved by implementing the selected LM tools. The performance improvement variables that were significantly influenced were speed, flexibility and dependability. The third part (Chapter 6) analyzed the moderation effect of IC on the relationship between LM tools and operational performance. The industries grouped under low IC were pharmaceutical, agrochemicals, steel, automobile, timber production, battery, chemical and plastics. The high IC industries were food, beverage, electronics and garment. A structural equation model was proposed and investigated across the two groups. A structured survey questionnaire was used to collect empirical data from manufacturing companies. The data obtained from the responses was analysed using Smart PLS 3 and SPSS version 25. The results of the study showed that IC had a moderating effect on the relationship between LM practices and operational performance for both low and high IC industries. The last chapter summarises the findings, made recommendations and proposes directions for further research. / Thesis (PhD)--University of Pretoria, 2019. / Organization for Women in Science for the Developing World (OWSD) / Industrial and Systems Engineering / PhD / Unrestricted

Thesis (M.A. (Clinical Psychology)) --University of Limpopo,2019 / Sociocultural pressures, including the thin-ideal internalization, and other aspects of self-objectification, are associated with body dissatisfaction. However, there is limited research regarding the association between self-objectification and engagement in health related behaviours among African females. A quantitative study was conducted with a sample of 411 female African university students from the University of Limpopo, South Africa to investigate the relationship between internalisation of sociocultural beauty standards and body dissatisfaction and engagement in health related behaviours. The study further explored whether cultural identity would moderate the relationship between internalisation of socio-cultural beauty standards and both body dissatisfaction and engagement in health related behaviours. Structural equation modelling (SEM) suggested that internalization of socio-cultural beauty standards significantly predicted students’ body satisfaction. No statistically significant relationship was found between internalization of socio-cultural beauty standards and engagement in health related behaviours. Also, cultural identity did not moderate the relationship between self-objectification and both body dissatisfaction and engagement in health related behaviours. This being a single study, further research is required to determine the relationship between the variables.

Telecommunicators (e.g., dispatchers and 911 operators) experience firsthand the death and suffering of friends, family, peers, and strangers in a chaotic work environment characterized by chronic stress and lack of support. Previous research has demonstrated telecommunicators are at increased risk for negative health outcomes; however, existing research does not identify predictive pathways to posttrauma symptoms in telecommunicators. In an application of the transactional theory of stress and coping, I used structural equation modeling to examine occupational antecedents, work-family conflict, negative appraising, and coping as predictors of posttraumatic stress symptoms in telecommunicators. A convenience sample of 103 telecommunicators, recruited through agencies across the United States, completed a series of PTSD, stress, and coping surveys. Results supported three theorems from the transactional theory of stress and coping: (a) Chronic antecedents are correlated with work-family conflict (r = .54, p < .01), (b) work-family conflict predicted negative appraising ( β = .64, p < .01), and (c) coping predicted posttraumatic stress symptoms in telecommunicators ( β = .30, p = .01). These findings contribute to the current body of occupational health literature by expanding understanding of telecommunicators'€™ occupational experiences and appraisals and provide insights into modifiable processes and policies that can enhance and protect telecommunicator long term health. Specifically, employee-focused policies directed at preserving work-home balance and reducing chronic stressors in the workplace are recommended. Additionally, further research can be initiated to evaluate effectiveness of policy changes in telecommunicator appraising, health, and wellbeing.

This dissertation presents an empirical investigation of learning from online courses. The current dissertation examined student participation, using Arbaugh et al.'s Community of Inquiry (CoI) survey instrument and Biggs et al.'s revised version of the Study Process Questionnaire (R-SPQ-2F) to determine CoI influences on learning from the students' perspective. This study is in response to Rourke and Kanuka's call to provide further empirical evidence about CoI conceptual framework connections to deep and meaningful learning. The purpose of this study was to examine the impact of the elements of CoI, cognitive, social, and teaching presences and students' learning approaches to students' perceived learning. Students enrolled in traditional, online, and, blended courses during the 2016 spring semester at a southwestern university participated in a web-based survey. Structural equation modeling was used to test the indirect effects between the elements of CoI, learning approaches, and perceived learning. Student's deep approach to learning was found to have an indirect effect between cognitive presence and perceived learning. However, this study's findings, when the CoI framework was viewed in its entirety, failed to provide evidence to simulate deep and meaningful learning.

京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第21188号 / エネ博第362号 / 新制||エネ||71(附属図書館) / 京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻 / (主査)教授 石原 慶一, 教授 東野 達, 教授 吉田 純 / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DFAM

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Thesis advisor: Lillie L. Albert / Increasing students' intentions to pursue mathematics-intensive careers is an urgent priority in the United States. To foster these intentions among marginalized student groups, such as immigrant students, and achieve equity in their career options, a critical question is whether we should allocate a greater proportion of school resources to mathematics over other subjects. The aims of this dissertation study were, first, to conceptually model and statistically evaluate how a school environment that prioritizes mathematics over other subjects might influence students' intentions to pursue mathematics over other academic subjects in the long term, and second, how this relationship is mediated by students’ mathematics pursuit attitudes, subjective norms, and perceived behavior (Ajzen, 1991), and moderated by their immigrant standing. The data for this study stemmed from the U.S. 2012 Programme For International Student Assessment Academic & Science (PISA) Student Questionnaire and School Questionnaire. A predictive mean matching technique was used to impute missing data that would resemble observed data. A 2-1-1 multilevel mediation Structural Equation Modeling (SEM) was implemented to accurately measure a school-level effect and student-level effect of the relationship of the examined constructs and to test the hypothesized model for the total sample. In order to compare immigrant student group and non-immigrant student group in the path model, multiple group path analysis was conducted. The results of the multilevel SEM model for the total sample presented that, at the school level (level 2), the school’s mathematics resources had no statistically significant direct and indirect effects on aggregated students’ intentions to pursue mathematics over other subjects. However, at the student level (level 1), students’ experiential and instrumental attitudes toward the pursuit of mathematics were positively related to students’ intentions to pursue mathematics over other subjects. The results of the multiple group path analysis comparing immigrant and non-immigrant student groups also found that the school’s mathematics resources had no statistically significant direct and indirect effects on students’ intentions to pursue mathematics over other subjects. However, a statistical difference in the overall path model of these two groups was found. The implications of this study for researchers, educators, and policymakers were discussed. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Lynch School of Education. / Discipline: Teacher Education, Special Education, Curriculum and Instruction.

Law enforcement administrators are concerned about the levels of public satisfaction with the police as a key to successful policing. Citizens who are satisfied with the police are more willing to provide cooperation with the police that is essential for the organization to reduce crime and serve the community effectively. Existing empirical studies have shown that citizen demographic characteristics and police performance are predictors of satisfaction with the police. The limitation of the previous studies, however, is that they did not consider what police agencies can do, specifically how they change or determine police performance. Without the organizational-level consideration, we may falsely blame individual police officers and their policing activities for the current elevated tension between the public and the police. This research attempts to address the void in the existing literature by introducing an extended theoretical framework that is structured with organizational-level predictors built upon already identified individual-level relationships with public satisfaction with the police.Using the Law Enforcement Organizations (LEO) survey A and Police-Community Interaction (PCI) survey of the National Police Research Platform Phase II, 2013–2015, at the organizational level, the current research examines the indirect associations between organizational characteristics (i.e., transformational leadership and organizational justice) of police agencies and public satisfaction with the police. Police job satisfaction and the proxy measures of police job performance (i.e., satisfaction with the specific police contact and perception of neighborhood safety) are the intervening variables in the relationship. In the current research, the merged data, including 16,547 citizens from 52 police agencies, are used for the analyses. The primary statistical approaches for the examination include factor analyses for the measurement model, bivariate analyses, and Multilevel Structural Equation Modeling (MSEM). The major finding of this research is that organizational justice, which is about the fairness of organizational behaviors, has an indirect association with public satisfaction with the police through police job satisfaction and citizen perceptions of neighborhood safety. This finding indicates that not only are individual police officers who encounter citizens and provide services able to shape citizen perceptions of the police, but police agencies and their administrators are able to actively improve the levels of satisfaction with the police overall.

The dynamic structural equation modeling (DSEM) framework incorporates hierarchical latent modeling (HLM), structural equation modeling (SEM), time series analysis (TSA), and time-varying effects modeling (TVEM) to model the dynamic relationship between latent and observed variables. To model the functional relationships between variables, a Gaussian process (GP), by definition of its covariance function(s), allows researchers to define Gaussian distributions over functions of input variables. Therefore, by incorporating GPs to model the presence of significant trend in either latent or observed variables, this dissertation explores the adequacy and performance of GPs in manipulated conditions of sample size using the flexible Bayesian analysis approach. The overall results of these Monte Carlo simulation studies showcase the ability of the multi-output GPs to properly explore the presence of trends. Also, in modeling intensive longitudinal data, GPs can be specified to properly account for trends, without generating significantly biased and imprecise estimates.

Establishing causal relationships is arguably the most important task of the social sciences. While the relationship between the social sciences and the concept of causality has been rocky, the randomized experiment gives us a concrete definition of a causal effect as the difference in outcomes due to the researcher's intervention. However, many interesting questions cannot be easily examined using experiments. Feasibility and ethics limit the use of randomized experiments in some situations and retrospective questions, i.e., working from the observed outcome to uncover the cause, require a different logic. Observational studies in which we observe pairs of variables without any intervention lend themselves to such situations but come with many difficulties. That is, it is not immediately clear whether an observed relationship between two variables is due to a true causal effect, or whether the relationship is due to other common causes. Panel data describe repeated observations of the same units over time. They offer a powerful framework for approaching causal questions with observational data. Panel analysis allows us to essentially use each unit as their own control. In an experiment, random assignment to either treatment and control group makes both groups equal on all characteristics. Similarly, if we compare the same individual pre- and post-treatment, then the two are equal at least on the things that do not change over time, such as sex, date of birth, nationality, etc. Structural equation modeling (SEM) is a group of statistical methods for assessing relationships between variables, often at the latent (unobserved) variable level. The use of SEM for panel analysis allows for a great deal of flexibility. Latent variables can be incorporated to account for measurement error and rule out alternative models. This dissertation focuses on the use of panel data in SEM for causal analysis. It comprises an introduction, four main chapters and a conclusion. After a short introduction (Chapter 1) outlining the goals and scope of the dissertation, Chapter 2 provides an overview of the topic of causality in the social sciences. Since the randomized experiment is often not feasible in social research, special emphasis has been placed on non-experimental, i.e., observational data. The chapter outlines some competing views on causality with non-experimental data, then discusses the two currently dominant frameworks for causal analysis, potential outcomes and directed graphs. It goes on to outline empirical methods and notes their compatibility with SEM. Chapter 3 discusses how panel data can be used to deal with unobserved time-invariant heterogeneity, i.e., stable characteristics that might normally confound analyses. It attempts to show in detail how basic panel regression in SEM works. It also discusses some issues that are not normally addressed outside of SEM, e.g., measurement error in observed variables, effects that change over time, model comparisons, etc. This discussion of the more basic panel regression setup provides a sort of basis for the more complex discussion in the following chapters. Chapter 4 compares and contrasts several ways to model dynamic processes, where the outcome at a particular point in time may affect future outcomes or even the presumed cause later on. It shows that popular recently proposed modeling techniques have much do to with their older counterparts. In fact, the newer modeling techniques do not seem to offer benefit with regards to estimating the causal effects of interest. The chapter focuses on arguably common situations in which the newer techniques may have serious drawbacks. Chapter 5 provides an applied example. It looks to better assess the causal effect of environmental attitudes on environmental behaviour (mobility, consumption, willingness to sacrifice). It touches on many of the aspects from the previous chapters, including the use of latent variables for constructs that are not directly observable, unobserved time-invariant confounders, state dependence (feedback from outcome to outcome), and reverse causality (feedback from outcome to cause). It shows that failure to account for time-invariant confounders leads to biased estimates of the effect of attitudes on behaviour. After controlling for these factors, the effects disappear in terms of mobility and consumption behaviour: when a person's attitudes become more positive, their behaviour does not become more environmentally-friendly. There is, however, a fairly robust effect of attitudes on willingness to sacrifice, even after controlling for unobserved time-invariant confounders, state dependence and reverse causality. This suggests changing attitudes do affect willingness to make sacrifices, holding potential time-invariant confounders, outcome to outcome feedback (essentially habits), as well as some time-varying confounders constant. Finally, Chapter 6 summarizes the previous chapters and provides an outlook for future work.:1. Introduction 2. Causal Inference in the Social Sciences 3. A Closer Look at Random and Fixed Effects Panel Regression in Structural Equation Modeling Using lavaan 4. Equivalent Approaches to Dealing with Unobserved Heterogeneity in Cross-Lagged Panel Models? 5. Re-Examining the Effect of Environmental Attitudes on Behaviour in a Panel Setting 6. Conclusion