Doctor of Philosophy, University of Toledo, 2010, Manufacturing Management

Over the past decade, an important focus of researchers has been on supply chain management (SCM), as many organizations believe that effective SCM is the key to building and sustaining competitive advantage for their products/services. To manage the supply chain, companies need to adopt an SCM strategy (SCMS) and implement appropriate SCM practices. However, different SCM strategies and practices require support from appropriate information technology (IT) applications and their usage. To effectively manage the supply chain, there is therefore a need for aligning these applications and their usage with the supply chain strategy and practices of the firm. While the literature on IT-business alignment has mainly focused on various aspects of the alignment between information systems (IS) strategy (ISS) and business strategy, it is largely deficient in offering an understanding of how specific supply chain strategies should be aligned with relevant IS strategies. Similarly, prior studies on SCM have developed considerable detail on supply chain strategies, without enunciating the implications of these strategies for the use of IT. Additionally, many studies have examined the importance of implementing SCM practices and their impact on supply chain and firm performance without identifying the corresponding IS usage that might be required for executing those practices in a more effective manner. Thus, there are no studies that explore the alignment between SCMS and ISS and between SCM practices and the usage of IT. This study contributes to the literature of SCM and IT by examining the alignment between the supply chain and information systems (SC-IS) at two levels. First, it looks at different SCM strategies and assesses appropriately- aligned information strategies that would enhance their effectiveness vis-a-vis their effect on the supply chain and firm performance; this is what this study refers to as alignment at the planning/strategic level. Second, the study look (open full item for complete abstract)

Committee: Monideepa Tarafdar PhD (Committee Co-Chair); T.S. Ragu-Nathan PhD (Committee Co-Chair); Thuong Le PhD (Committee Member); Hokey Min PhD (Committee Member) Subjects: Information Systems; Management

Doctor of Philosophy, University of Akron, 2021, Engineering

Lean Management Systems (LMS) and Continuous Improvement (CI) program deployments are increasingly becoming an important strategy for many organizations for gaining competitive advantage. Such deployments promise success to organizations of any nature and size. Significant resources in the form of employee time, external consultants and training programs are spent on LMS/CI deployments by a vast number of companies every year. Yet despite the long history and evolution of CI methodology, ease of concepts and application, high amount of time and resources spent and furthermore proliferation of such deployments - the adoption and ultimately the success of such programs is highly variable. While many research papers and companies claim to have realized hundreds of millions in economic benefits from Lean deployments, just as many report to not even recover the cost of deployment. Researchers conclude that a complete systems approach to successfully deploy Lean methodology for long-term sustained gains is not widely understood or practiced. For this reason, in this research I present the design work of a new way to approach Lean/CI deployment methodology utilizing first-principles. The need for the new approach to deploying Lean/CI was engendered due to a mandate from the Senior Leadership Team (SLT) at the organization. The SLT desired a holistic approach that would align better to Company's Lean principles (first principles), show financial impact and bring about behavioral cultural change. This research presents the new approach - from First-Principles for the methodology, design criterion from SLT, to the design of the methodology, and then the application of the methodology to different businesses in the company. In essence, the research shows how any organization can build its own LMS utilizing first- principles to fit their own needs rather than copy pasting fragmented components offered by expensive consultants or snippets from literature – neither of w (open full item for complete abstract)

Committee: Shengyong Wang (Advisor); Asoke Dey (Committee Member); Xiaosheng Gao (Committee Member); Ping Yi (Committee Member); Chen Ling (Committee Member) Subjects: Banking; Engineering; Industrial Engineering; Management; Mechanical Engineering; Systems Design; Systems Science

Doctor of Philosophy (PhD), Ohio University, 2017, Clinical Psychology (Arts and Sciences)

Teachers routinely report they are under-prepared for the classroom management challenges of the modern classroom and pre-service training programs are an important point of intervention. However, few studies have investigated classroom management training at the pre-service level. The present study compares two different training approaches across a single semester: (1) an internship-as-usual condition that includes mentorship by an in-service teacher and placement in a Pre-K through 3rd grade classroom: or (2) an intensive coaching condition that includes all activities from the internship-as-usual, plus experiential learning, observations, and performance feedback four times throughout the semester. Twenty-four senior undergraduate teacher trainees were randomly assigned to one of the two conditions. Results indicated that, on average, teachers in the intensive coaching condition demonstrated significantly greater improvement in several observed classroom management skills over time (e.g., concept summaries and behavioral praise); and that the magnitude of group differences in endpoint skills was moderate to large for all skills (between group effect sizes ranged from .44 to 1.31) Self-report measures (of knowledge, stress, self-efficacy for teaching, problems solving of classroom management challenges) showed little change over time and minimal group differences. Although both groups were satisfied with their experience, those in the intensive condition reported moderately more growth and satisfaction.

Committee: Julie Owens (Advisor) Subjects: Psychology; Teacher Education; Teaching

Master of Science, The Ohio State University, 2022, Electrical and Computer Engineering

Heavy-duty trucks idling during the hoteling cycle consumes millions of gallons of diesel a year, which creates negative impact on economy and environment. Researchers have been taking efforts to avoid idling by finding alternative energy sources for HVAC and auxiliary loads during the hoteling cycle: SuperTruck 2 project aims to save fuel by hybridizing a class-8 truck with a transmission mounted 48V electrical charging system, which regenerates power from road loads to charge the battery. All the auxiliaries, including HVAC for the hoteling cycle, are electrically connected to and supported by the battery. The previous research studied on this powertrain and analyzed drivers' usual driving cycle and electric loads and conducted a backward simulation with dynamic programming strategy to calculate for the optimal control inputs. Finally, there was a proposed solution: an ideal energy management strategy controlling the hybrid mode and EM torque for the desired battery's State of Charge (SoC). Such solution, however, is more valuable as an ideal benchmark, rather than an implementable real-time based controller on real trucks. To test the optimal solution in a ”driver-to-wheel” schematic, new control strategies should be applied to a forward simulator, which is the work presented in this thesis. The control strategies developed and analyzed in this thesis are tested on a forward simulator created in MATLAB Simulink, which is customized for the same class-8 48V ii mild-hybrid powertrain. In contrast to backward simulator, the forward simulator starts everything with the driver's action to keep the vehicle speed at the prescribed velocity profile, and then the simulator responds with the actual speed as a result of the control inputs. The forward simulator is in the form of a feedback loop, where the driver is reacting to the difference between actual and desired speeds. Besides the driver, a mode switch controller signals for mode changes and enables the (open full item for complete abstract)

Committee: Qadeer Ahmed (Advisor); Athar Hanif (Other); Lisa Fiorentini (Committee Member) Subjects: Electrical Engineering

Bachelor of Arts, Wittenberg University, 2019, Business

Actively managed mutual funds are some of the most invested in investment vehicles in the modern era. However, it is a great misunderstanding of their performance relative to their passively managed exchange-traded funds. Actively managed mutual funds fail to outperform their respective benchmarks due to a variety of reasons including market efficiency, timing, and tax consequences. These findings hold true in both the long-term and short-term for equities and fixed income funds. A self-conducted survey was also conducted in order to find the knowledge and opinions of college students on ETFs and mutual funds.

Committee: Rachel Wilson (Advisor); Lawrence Gwinn (Committee Member); Kevin Steidel (Committee Member) Subjects: Finance

Doctor of Philosophy, The Ohio State University, 2019, Mechanical Engineering

This dissertation examines the benefit of energy optimization in the operation of a vehicle system at an individual vehicle level and the fleet level. For energy optimization in an individual vehicle, a hybridized Class 6 Pickup and Delivery truck with a Range Extended Electric Vehicle configuration is considered. The truck's components were chosen for minimal energy consumption while meeting all the performance requirements of a conventional, diesel-powered vehicle of that class and application. Dynamic Programming is used to determine the best possible energy consumption performance over the course of a working day for the hybrid truck. Energy consumption is then determined using a causal energy management controller on a forward simulator that is compatible with implementation in real-time, where this dissertation introduces the use of a distance-based driver that accurately matches the distance traveled by the vehicle from every start-to-stop in the drive cycle even if the performance constraints of the components prevent the exact matching of the drive cycle speed. The energy consumption results with the forward simulator demonstrate that with increasing levels of information of the expected duty cycle of the day, the onboard energy management can be easily adapted to obtain better fuel consumption performance. For energy optimization in a vehicle fleet, a delivery vehicle fleet is considered that consists of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs) and conventional Internal Combustion Engine Vehicles (ICEVs) operating over the same service area, from a shared depot. This dissertation develops a methodology for route optimization of such a heterogeneous delivery vehicle fleet while taking into account information related to static parameters of the service area (such as topography, payload and driving distance) and dynamic driving conditions (such as traffic incidents and traffic lights). The benefit of route optimization of the fleet f (open full item for complete abstract)

Committee: Giorgio Rizzoni PhD (Advisor); Qadeer Ahmed PhD (Committee Member); Shawn Midlam-Mohler PhD (Committee Member); Marcello Canova PhD (Committee Member); Ran Dai PhD (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering

Master of Arts (MA), Ohio University, 2019, Latin American Studies (International Studies)

This thesis strives to analyze the fast casual food market in order to understand if Chipotle would be successful in Brazil and what the concerns would be if the American chain decides to internationalize its operation to Brazil. This thesis has developed as a prospective case study based mainly on business and cultural analysis. The study analyzed Brazil by using the theory of market assessment, and also analyzed the fast casual, fast food, and food service industries and products by applying other business theories such as PESTEL, Porter Five Forces and VRIO. Furthermore, the study interviewed one entrepreneur in the fast casual industry in Belo Horizonte, Minas Gerais, Brazil to understand the local industry dynamics. Regarding all the information analyzed, this thesis creates a business model that consists on explaining how Chipotle would operate in the country. Brazil is an attractive market for Chipotle, there is room for rapid growth, and Chipotle could provide a positive impact for Brazilians.

Committee: Arthur Hughes (Committee Member); Patricia Toledo (Committee Chair); Grigorios Livanis (Committee Member); Michael Geringer (Advisor) Subjects: Business Administration; Business Community; Business Costs; Business Education; Cultural Resources Management; Economics; Finance; International Law; International Relations; Latin American Studies

Master of Science, The Ohio State University, 2018, Mechanical Engineering

The performance of a powertrain is dependent on how its controller parameters are tuned for the given duty cycle. In real world, the duty cycle is hard to predict, however, if the historical performance is evaluated and used to fine tune the controller parameters, the performance of the powertrain can be improved. This thesis presents a retrospective information-based powertrain performance improvement. The performance is evaluated using the fuel economy and charge sustaining operation of an electrified powertrain. A Retrospective Cost Adaptive Controller (RCAC) has been designed for parallel HEV, which improves it performance based on the former performance. The simulation results demonstrate that the controller parameters are re-calibrated to show improved performance for standard test driving cycles as well as cycles with different driving styles. A dynamic programming (DP) solution is also given as a benchmark to evaluate RCAC results.

Committee: Giorgio Rizzoni Prof. (Advisor); Vadim Utkin Prof. (Committee Member); Qadeer Ahmed Dr. (Committee Member) Subjects: Mechanical Engineering

Master of Science, University of Toledo, 2017, Civil Engineering

In the United States, aging of the transportation infrastructures increased the need of M&R activities significantly. Further, as the consequence of economic and population growth, increasing travel demand accelerated the rate of deterioration along with the congestion and poor safety. At most of the highway agencies, budget allocation decisions are often based on the historical proportions or empirical relations. However, the public awareness and involvement in the sustainable development demand the accountability of such decision- making. Compounded with the budget constraints and rise in the highway improvement costs, highway agencies have a need of efficient and most cost-effective decision-making. Thus, development of a data-driven tool as a senior decision support system is essential for short-term as well as long-term planning, specifically, in a limited available resource scenario such as of now. This thesis presents the development and implementation of a trade-off analysis tool in Transportation Asset Management (TAM). The tool is capable of budget allocation and optimal treatment policy determination, based on the budget versus asset performance plots. For a given budget level, the corresponding performance of the asset network is determined using the linear programming optimization with the objective of average network condition maximization. The optimization model essentially consists of an asset deterioration model. The deterioration rates are predicted based on the historical condition data using the Markov model. At the network level, the macroscopic model is adopted where each decision variables represents a specific proportion of the asset network. For each decision variable, future condition prediction models are developed. An annual budget scenario can be analyzed with the flexibility to vary the treatment unit costs, allowable treatments, treatment average life, the budget allocation between asset types, and the span of the analysis period. (open full item for complete abstract)

Committee: Eddie Chou Dr. (Committee Chair); Liangbo Hu Dr. (Committee Member); Habib Kaake Dr. (Committee Member) Subjects: Civil Engineering

Doctor of Philosophy, The Ohio State University, 2017, Mechanical Engineering

The dissertation offers a systematic analysis on the interdependency between fuel economy and battery capacity degradation in hybrid electric vehicles. Optimal control approaches including Dynamic Programming and Pontryagin's Minimum Principle are used to develop energy management strategies, which are able to optimally tradeoff fuel consumption and battery aging. Based on the optimal solutions, a real-time implementable battery-aging-conscious Adaptive Equivalent Consumption Management Strategy is proposed, which is able to achieve performance that is comparable to optimal results. In addition, an optimal control based charging strategy for plug-in hybrid electric vehicles and battery electric vehicles is developed, which minimizes battery capacity degradation incurred during charging by optimizing the charging current profile. Combining a generic control-oriented vehicle cabin thermal model with the battery aging model, the benefit of this strategy in terms of decreasing battery aging is significant, when compared with the existing strategies, such as the widely accepted constant current constant voltage (CC-CV) protocol. Thus this dissertation presents a complete set of optimal control solutions related to xEVs with consideration of battery aging.

Committee: Giorgio Rizzoni (Advisor) Subjects: Automotive Engineering; Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2016, Mechanical Engineering

One of the main goals of hybrid electric vehicle technology is to improve the energy efficiency. In industry and most of academic research, the powertrain control is designed and evaluated under standard driving cycles. However, the situations that a vehicle may encounter in the real world could be quite different from the standard cycles. Studies show that the human drivers have a great influence on the vehicle energy consumptions and emissions. The actual operating conditions that a vehicle faces are not only dependent on the roads and traffic, but also dependent on the drivers. A standard driving cycle can only represent the typical and averaged driving style under the typical driving scenarios, therefore the control strategies designed based on a standard driving cycle may not perform well for all different driving styles. This motivates the idea to design optimally-personalized hybrid electric vehicle control methods that can be adaptive to individual human driving styles and their driving routes. Human-subject experiments are conducted on a driving simulator to study the driving behaviors. A stochastic driver pedal model that can learn individual driver's driving style is developed first. Then a theoretic investigation on worst-case relative cost optimal control problems, which is closely related to vehicle powertrain optimal control under real-world uncertain driving scenarios, is presented. A two-level control structure for plug-in hybrid electric vehicles is proposed, where the parameters in the lower-level controller can be on-line adjusted via optimization using historical driving data. The methods to optimize these parameters are designed for fixed-route driving first, and then extended to multi-routes driving using the idea similar to the worst-case relative cost optimal control. The performances of the two proposed methods are shown through simulations using human driving data and stochastic driver model data respectively. The energy consumption resul (open full item for complete abstract)

Committee: Junmin Wang (Advisor); Ryan Harne (Committee Member); Chia-Hsiang Menq (Committee Member); Haijun Su (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Doctor of Philosophy (PhD), Ohio University, 2016, Industrial and Systems Engineering (Engineering and Technology)

This dissertation aims to fulfill the gap of designing the supply chain system as a whole and looking at overall design across the supply chain of the company in the long term rather than short term. This dissertation is inspired from the window manufacturer which manufactures and distributes vinyl windows to meet new construction and replacement/remodeling sector demand. In this dissertation, complementary analytical models are discussed to determine efficient way to design a supply chain network. Mainly, design aspect and operational aspect of a supply chain system are considered. In the design aspect, number of manufacturing facilities, location/allocation decisions are determined. Then, the number of distribution centers, location and allocation decisions are made. Continuing with that, manufacturing configuration of each individual manufacturing facility is designed in detail and analyzed. In the proposed layered cellular manufacturing system design, based on the demand and processing requirements, products are grouped into product families and assigned to dedicated, shared and remainder cells. In the operational aspect, based on the designed manufacturing system, cell loading and product sequencing are performed. Moreover, vehicle routing system is designed to reach out the end customers in the supply chain system. All in all, this dissertation is unique in the sense of covering different levels of supply chain planning and decisions with nested approaches of facilities location, manufacturing system design, network design and vehicle routing design. New mathematical models and various new heuristic approaches are proposed to design a supply chain system in the presence of high-volume and low-volume windows demand.

Committee: Gürsel A. Süer PhD (Advisor); Faizul Huq PhD (Committee Member); M. Khurrum Bhutta PhD (Committee Member); Dale Masel PhD (Committee Member); Diana Schwerha PhD (Committee Member) Subjects: Engineering; Industrial Engineering; Operations Research

Doctor of Philosophy, The Ohio State University, 2015, Mechanical Engineering

In today's world, automotive manufacturers face the difficult challenge of building vehicles that are capable of meeting the increasingly stringent fuel economy and emissions standards, while also maintaining the performance and drive quality that consumers have come to expect. The automotive industry's response to this has been to make increasingly advanced vehicles that require more complex control systems, often resulting in longer development times and higher costs. One way to help reduce the development time and cost associated with these advanced vehicles is to use a model-based design approach. This approach allows engineers to design more of the vehicle's control system in a virtual environment, before hardware is available to test the control software. While model-based design techniques have helped reduce the amount of development time and cost that is needed to design the control system for a vehicle, these model-based techniques may not fully account for a vehicle's drive quality characteristics. Many of the energy management optimal control algorithms for hybrid vehicles designed in virtual environments today are capable of achieving high fuel economy numbers, but may result in poor drive quality characteristics when implemented on a vehicle. Therefore, a new methodology is needed to account for a vehicle's drive quality during the initial stages of a vehicle's control development. The research presented here describes a new methodology where drive quality metrics are added to the optimal control algorithm's cost function, in order to allow the algorithm to find a good balance between fuel economy and drive quality. Although some research has been previously published in this area, the majority of research does not specifically link the criteria used to improve drive quality to the physical behavior of the vehicle. Other research solves the optimal energy management problem to minimize fuel consumption, but then filters the results to prevent dri (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Shawn Midlam-Mohler (Committee Member); Wei Zhang (Committee Member); Manoj Srinivasan (Committee Member) Subjects: Mechanical Engineering

MDES, University of Cincinnati, 2011, Design, Architecture, Art and Planning: Design

This thesis focuses on the interaction between design and business, exploring its impact on the success of organizations through two case studies of design managers, Dan Harden, Chief Executive Officer for Whipsaw Inc and Sam Lucente, Global Vice President of Design for Hewlett-Packard. Through an analysis of organizational strategy and design, this thesis proposes a theoretical model that identifies how design becomes a dynamic capability for any organization when its promotion and support shifts from a person to a function. Finally, based on this model, this thesis analyzes the effectiveness of design thinking in supporting design as a dynamic capability and offers conclusions for the elevation of a design function in support of a sustained competitive advantage in organizations.

Committee: Craig Vogel MD (Committee Chair); Dale Murray MA (Committee Member); Martha Ann Welsh PhD (Committee Member) Subjects: Design

Doctor of Philosophy, University of Toledo, 2009, Electrical Engineering

Series hydraulic hybrid technology has the potential to significantly improve fuel economy and reduce emission. The series hydraulic hybrid is very different from electric and parallel hydraulic configuration and requires a unique power management control strategy to realize its optimal potential. In this dissertation, three approaches to achieve optimality are proposed and analyzed. These are rule-based, intelligent, and mixed power management control strategy. For evaluating the performance of control strategies, a forward-facing closed-loop simulation model based on physical features is first established in the MATLAB/SIMULINK environment. We then introduce a simple, valid and easily implementable rule-based power management control strategy. To derive the control signals, a PID-based multi-stage controller is presented. A thorough analysis on a class VI medium truck is elucidated. The simulation results demonstrate that a series hydraulic hybrid medium truck with the proposed rule-based power management control strategy results in fuel economy increases of 117% and 44% over the conventional baseline respectively over Federal Urban Driving Schedule (FUDS) and Federal Highway Driving Schedule (FHDS). Then, an intelligent power management control strategy incorporating artificial neural networks (ANNs) and dynamic programming (DP) algorithm applied to series hydraulic hybrid propulsion systems is presented. ANNs are used to forecast vehicle speed and DP is utilized to find the optimal control actions for gear shifting and dual power source splitting. A thorough analysis of effect on fuel economy with different prediction window size on the class VI medium truck over FUDS and FHDS is presented. Compared with conventional baseline, the simulation results demonstrate that series hydraulic hybrid medium truck with 20 seconds short-term prediction window enables fuel economy increase of 135% and 48% respectively over FUDS and FHDS. Although the intelligent power mana (open full item for complete abstract)

Committee: Roger King (Committee Chair); Walter Olson (Committee Co-Chair); Thomas Stuart (Committee Member); Richard Molyet (Committee Member); Gursel Serpen (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2012, Electrical and Computer Engineering

This thesis presents the optimal control problem of energy management in a plug-in hybrid electric vehicle. Review of the literature suggests the need for a methodology which follows a blended strategy unlike the traditional charge depleting - charge sustaining (CD-CS) strategy for state of charge of the battery. Many present blended strategies require a-priori knowledge of the driving mission which is obtained by prediction. The performance of these strategies again depends on the prediction algorithms and often end up being sub-optimal in implementation. There is a need for an energy management strategy that provides near optimal results with minimal information about the driving mission. This thesis proposes one such controller. Knowledge of the optimal trajectories under various driving conditions is obtained by implementing a Pontryagin's Minimum Principle (PMP) based energy management scheme. With this knowledge, a practical implementable controller is proposed which performs with near optimal results under different driving missions. A comparison of the optimal PMP solution, the practical controller solution and the traditional CD-CS solution is done to conclude this work.

Committee: Giorgio Rizzoni PhD (Advisor); Yann Guezennec PhD (Advisor); Simona Onori PhD (Advisor); Mahesh Illindala PhD (Committee Member) Subjects: Alternative Energy; Automotive Engineering; Electrical Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2008, Labor and Human Resources

This dissertation focuses on a deceptively simple question: To what extent are received micro and macro HRM theories reconcilable? The premise underlying each of the following chapters is that perhaps the human capital theories micro and macro scholars use are a viable mechanism to bridge the micro and macro traditions. However, the essential elements (e.g., constructs) and structure (i.e., theory) required to test such a premise do not exist and must be created. Therefore, each chapter either creates a required element or contributes to the required structure.Chapter One provides an overview and specifies four assumptions underlying micro and/or macro theory that must be adapted to create an integrated theoretical model. Chapter Two creates the theory for the disaggregation of HRM systems into functional constructs. In Chapter Three, a definition of human capital is created that integrates micro and macro traditions. An initial test of the integrated theory is completed by subjecting a proprietary industry-specific dataset to hierarchical linear modeling. The focus of Chapter Four is: Does the human capital context of an organizational unit (e.g., an establishment) alter the assumed single-level human capital-performance relationships (e.g., a team human capital to team performance relationship) embedded within the organizational unit? The data support that human capital context sometimes moderates single-level human capital-performance relationships. The studies provide a starting point for future research on the extent to which human-capital research may serve as a mechanism to bridge micro and macro HRM. Such research is important because ecological fallacies may result if only micro theory is used to predict the performance of collectives and if the hierarchical structure in which human capital is embedded is not considered.

Committee: Jay Barney PhD (Committee Chair); Howard Klein PhD (Committee Member); Robert Heneman PhD (Committee Member); Sharon Alvarez PhD (Committee Member) Subjects: Management; Organization Theory; Organizational Behavior

Doctor of Philosophy, The Ohio State University, 2007, Business Administration

The purpose of this research was to determine how the decision to manage suppliers beyond Tier 1 was made in a supply chain context. This research explored the reasons for managing suppliers beyond Tier 1, when to manage them, how to manage them, how the decision to manage them was made, and how to measure performance. A total of seven companies were included in the research and 10 interviews were conducted. All seven are multi-billion dollar companies, with the unit price of finished products ranging from a few dollars to tens of thousand of dollars. They represented consumer packaged goods, durable goods, quick service restaurant chains, and retail industries. Data were collected from personal interviews. Data showed clear patterns in the motivations and strategies for managing suppliers beyond Tier 1. There also existed patterns in the associations between the motivations and the strategies. A normative model was developed on the basis of these patterns. Managers can use the normative model to identify the strategies for managing suppliers beyond Tier 1, and refer to the model with specific motivations and/or activities to establish their action plan. Based on the data collected, a framework of decision-making process was also developed, which can be used together with the normative model developed in this research for managing suppliers beyond Tier 1. The research has a number of implications for both practitioners and academicians. Based on the findings, many decisions in managing suppliers beyond Tier 1 could be improved if a normative model showing the associations between motivations and strategies was available to managers. Also, a framework of the decision-making process would be of significant assistance in managing suppliers beyond Tier 1. In addition, improved understanding of the complexity of a trilateral interorganizational relationship could help management recognize quick-wins in the task of managing suppliers beyond Tier 1.

Committee: DOUGLAS LAMBERT (Advisor) Subjects: Business Administration, General

Doctor of Philosophy, The Ohio State University, 2006, Business Administration

The main objective of this dissertation is to study the role of Six Sigma programs in deploying effective continuous improvement. Through three related essays we address three areas of inquiry focused on Six Sigma: (1) the place of Six Sigma in the evolution of continuous improvement programs, (2) organization level infrastructure that is critical for institutionalizing Six Sigma, and (3) practices used in Six Sigma projects for discovering process improvements. The first essay uses concepts from Nelson and Winter's (1982) theory of evolutionary economics to present a conceptual model for the emergence of new continuous improvement programs such as Six Sigma. Based on its descriptions in the literature, Six Sigma appears to be a logical next-step in the evolution of continuous improvement programs. There are apparent differences compared to previous programs in the way Six Sigma is structured in organizations and in the way its team-projects target improvements. In the second essay we employ the lens of the behavioral theory of the firm (Cyert and March, 1963) to derive a list of critical elements of organizational infrastructure for continuous improvement. Further, we analyze whether and how organizations that have deployed Six Sigma programs for continuous improvement cover these elements. We use empirical observations from interviews conducted with continuous improvement executives from five organizations that have deployed Six Sigma programs. We find mixed results regarding coverage of infrastructure in these organizations. Although the prescriptive practitioner-targeted literature on Six Sigma covers most of the infrastructure elements, organizations are neglecting some important elements that are critical for effective continuous improvement. The third essay empirically addresses the question of how knowledge creation activities (Nonaka, 1994) used in Six Sigma team-projects result in process improvements. Adapting existing scales for knowledge creation constr (open full item for complete abstract)

Committee: Peter Ward (Advisor) Subjects: Business Administration, Management

Doctor of Philosophy, The Ohio State University, 2004, Communication

The media industries play a major role in the trend of globalization today. Western transnational media corporations (TNMCs) have been actively expanding their businesses around the world for maximized profits. China's accession to WTO in 2001 demonstrated further openness of the economy to international competition. This study aimed to examine Western TNMCs' risk and strategies in the Chinese media market after WTO in an attempt to provide insights into the global media giants' perceptions, positions, and plans regarding the market. International business theories highlighted the effect of a firm's external conditions on its strategy. Examination of Western TNMCs' strategic behavior in transitional and emerging markets revealed that their equity ownership differs by location as affected by the level of risk they perceive in the market. Based on its external conditions, a firm's perception of risk in terms of uncertainties about the market affects its control strategy in terms of equity ownership. Employing this innovative theoretical model, the study aimed to determine whether China's WTO entry would lead to Western TNMCs' lowered uncertainty perceptions and higher equity ownership in China as compared to before WTO, and a. The primary research method used in this study was interviews. A total of 15 informants from Western TNMCs and 17 from Chinese media organizations and government agencies constituted the final sample. Results of the study suggested that Western TNMCs' risk perceptions showed no major discontinuity in China's post-WTO era. In addition, their perceptions were basically consistent with the actual conditions of the Chinese media market, as well as those of the Chinese media professionals and policy makers. These findings reflect the Chinese government's gradualism strategy in economic reforms and development, and in regulating the media industries. In response to the risk they perceive in a long run, Western TNMCs have plans for increasing control. (open full item for complete abstract)

Committee: John Dimmick (Advisor) Subjects: