Matching Items (6)
Filtering by
- All Subjects: Product Design
- Genre: Academic theses
- Creators: Dooley, Kevin
- Creators: Du, Xiaoping
- Member of: ASU Electronic Theses and Dissertations
Description
This study investigates how well prominent behavioral theories from social psychology explain green purchasing behavior (GPB). I assess three prominent theories in terms of their suitability for GPB research, their attractiveness to GPB empiricists, and the strength of their empirical evidence when applied to GPB. First, a qualitative assessment of the Theory of Planned Behavior (TPB), Norm Activation Theory (NAT), and Value-Belief-Norm Theory (VBN) is conducted to evaluate a) how well the phenomenon and concepts in each theory match the characteristics of pro-environmental behavior and b) how well the assumptions made in each theory match common assumptions made in purchasing theory. Second, a quantitative assessment of these three theories is conducted in which r2 values and methodological parameters (e.g., sample size) are collected from a sample of 21 empirical studies on GPB to evaluate the accuracy and generalize-ability of empirical evidence. In the qualitative assessment, the results show each theory has its advantages and disadvantages. The results also provide a theoretically-grounded roadmap for modifying each theory to be more suitable for GPB research. In the quantitative assessment, the TPB outperforms the other two theories in every aspect taken into consideration. It proves to 1) create the most accurate models 2) be supported by the most generalize-able empirical evidence and 3) be the most attractive theory to empiricists. Although the TPB establishes itself as the best foundational theory for an empiricist to start from, it's clear that a more comprehensive model is needed to achieve consistent results and improve our understanding of GPB. NAT and the Theory of Interpersonal Behavior (TIB) offer pathways to extend the TPB. The TIB seems particularly apt for this endeavor, while VBN does not appear to have much to offer. Overall, the TPB has already proven to hold a relatively high predictive value. But with the state of ecosystem services continuing to decline on a global scale, it's important for models of GPB to become more accurate and reliable. Better models have the capacity to help marketing professionals, product developers, and policy makers develop strategies for encouraging consumers to buy green products.
ContributorsRedd, Thomas Christopher (Author) / Dooley, Kevin (Thesis advisor) / Basile, George (Committee member) / Darnall, Nicole (Committee member) / Arizona State University (Publisher)
Created2012
Description
Industrial activities have damaged the natural environment at an unprecedented scale. A number of approaches to environmentally responsible design and sustainability have been developed that are aimed at minimizing negative impacts derived from products on the environment. Environmental assessment methods exist as well to measure these impacts. Major environmentally responsible approaches to design and sustainability were analyzed using content analysis techniques. The results show several recommendations to minimize product impacts through design, and dimensions to which they belong. Two products made by a manufacturing firm with exceptional commitment to environmental responsibility were studied to understand how design approaches and assessment methods were used in their development. The results showed that the company used several strategies for environmentally responsible design as well as assessment methods in product and process machine design, both of which resulted in reduced environmental impacts of their products. Factors that contributed positively to reduce impacts are the use of measurement systems alongside environmentally responsible design, as well as inspiring innovations by observing how natural systems work. From a managerial perspective, positive influencing factors included a commitment to environmental responsibility from the executive level of the company and a clear vision about sustainability that has been instilled from the top through every level of employees. Additionally, a high degree of collaboration between the company and its suppliers and customers was instrumental in making the success possible.
ContributorsHuerta Gajardo, Oscar André (Author) / Giard, Jacques (Thesis advisor) / White, Philip (Committee member) / Dooley, Kevin (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation is to address product design optimization including reliability-based design optimization (RBDO) and robust design with epistemic uncertainty. It is divided into four major components as outlined below. Firstly, a comprehensive study of uncertainties is performed, in which sources of uncertainty are listed, categorized and the impacts are discussed. Epistemic uncertainty is of interest, which is due to lack of knowledge and can be reduced by taking more observations. In particular, the strategies to address epistemic uncertainties due to implicit constraint function are discussed. Secondly, a sequential sampling strategy to improve RBDO under implicit constraint function is developed. In modern engineering design, an RBDO task is often performed by a computer simulation program, which can be treated as a black box, as its analytical function is implicit. An efficient sampling strategy on learning the probabilistic constraint function under the design optimization framework is presented. The method is a sequential experimentation around the approximate most probable point (MPP) at each step of optimization process. It is compared with the methods of MPP-based sampling, lifted surrogate function, and non-sequential random sampling. Thirdly, a particle splitting-based reliability analysis approach is developed in design optimization. In reliability analysis, traditional simulation methods such as Monte Carlo simulation may provide accurate results, but are often accompanied with high computational cost. To increase the efficiency, particle splitting is integrated into RBDO. It is an improvement of subset simulation with multiple particles to enhance the diversity and stability of simulation samples. This method is further extended to address problems with multiple probabilistic constraints and compared with the MPP-based methods. Finally, a reliability-based robust design optimization (RBRDO) framework is provided to integrate the consideration of design reliability and design robustness simultaneously. The quality loss objective in robust design, considered together with the production cost in RBDO, are used formulate a multi-objective optimization problem. With the epistemic uncertainty from implicit performance function, the sequential sampling strategy is extended to RBRDO, and a combined metamodel is proposed to tackle both controllable variables and uncontrollable variables. The solution is a Pareto frontier, compared with a single optimal solution in RBDO.
ContributorsZhuang, Xiaotian (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Zhang, Muhong (Committee member) / Du, Xiaoping (Committee member) / Arizona State University (Publisher)
Created2012
Description
A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results.
The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages.
In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules.
In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression.
In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.
The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages.
In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules.
In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression.
In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.
ContributorsLee, Dongjin (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Wu, Teresa (Committee member) / Du, Xiaoping (Committee member) / Arizona State University (Publisher)
Created2018
Description
Original equipment manufacturers (buyers) are increasingly involving suppliers in new product development as a means to increase efficiency and expand capabilities. To realize such benefits, however, the two firms need to have appropriate communication and goal structures to minimize friction while maximizing design quality. In addition, the effectiveness of the inter-firm interaction process, i.e. their collaboration quality, is also a key success factor. This study draws from Information Process Theory to propose that higher technical and relational uncertainty requires more inter-firm communication. The misalignment between communication intensity and uncertainty reduces both design quality and design efficiency. Goal incongruence, which always lowers project performance, is less harmful for projects with high technical uncertainty due to the potential of the conflict resolving process in improving decision quality and efficiency. Finally I use Hackman's theory of work group effectiveness to propose that collaboration quality fully mediates the effects of communication intensity and goal congruence on project outcomes. The study used an empirical survey of manufacturers as the primary method of data collection. Manufacturers that integrate and assemble complex and discrete products are the target population. Design engineers and project managers from manufacturers were my target respondents. Both SEM and hierarchical regression were used to test the conceptual model. The dissertation made five theoretical contributions. First, I introduced the concept that there is an optimal level of inter-firm communication intensity, exceeding which lowers design efficiency without improving design quality. Second, I theoretically defined and empirically operationalized two types of uncertainty, one on the project level and one on the inter-firm level, which were shown to moderate the effects of inter-firm communication and goal structures on collaboration outcomes. Third, this study examined the conditions when goal congruence is more effective in improving collaboration outcomes. Fourth, this study nominally and operationally defined collaboration quality, a theoretical construct which measure the effectiveness of inter-partner interactions rather than mere existence or amount of certain activities pursued by partners. Finally, I proposed several enhancements to existing construct measures.
ContributorsYan, Tingting (Author) / Dooley, Kevin (Thesis advisor) / Choi, Thomas (Committee member) / Carter, Joseph (Committee member) / Arizona State University (Publisher)
Created2011
Description
Design for sustainability and design to change habits are two areas that have been explored separately. Design for sustainable behavior has started to be researched for different purposes. This research focuses on how we interact with objects to reinforce sustainable actions, focused on low-waste drinking water consumption using Water Bottle Filling Stations. Things do not work the same in different contexts, even if they are targeted at a similar group of people in two different countries. In consequence, the habits around particular objects change as well. This research is part of a bi-cultural study on the relationship between users and Water Bottle Filling Stations in universities, sites where these devices have been installed to promote healthy habits and encourage sustainable practices in their population. This is to evaluate the use of current nudges attached to the design attributes on the artifact.Using mixed methods, this research explored the possibility of using Water Bottle Filling Stations to create and reinforce habits in the user’s routine and the consequences with the aid of nudges. To understand these behaviors, populations from a college in Mexico and a college in the United States were subjects of study to understand the implications of using Water Bottle Filling Stations as a device that, by design, promotes reusability as a circular economy strategy. The following research did not aim to redesign the entire system but evaluate the impact of current nudges and design attributes on the artifact, how habits have affected culture, and supply a list of findings and recommendations.
ContributorsBecerra-Galicia, Susana Angelina (Author) / Takamura, John (Thesis advisor) / Fehler, Michelle (Thesis advisor) / Dooley, Kevin (Committee member) / Arizona State University (Publisher)
Created2022