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- All Subjects: Business logistics
- Genre: Academic theses
- Creators: Dooley, Kevin
- Creators: Janakiram, Mani
- 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
Supply chains are increasingly complex as companies branch out into newer products and markets. In many cases, multiple products with moderate differences in performance and price compete for the same unit of demand. Simultaneous occurrences of multiple scenarios (competitive, disruptive, regulatory, economic, etc.), coupled with business decisions (pricing, product introduction, etc.) can drastically change demand structures within a short period of time. Furthermore, product obsolescence and cannibalization are real concerns due to short product life cycles. Analytical tools that can handle this complexity are important to quantify the impact of business scenarios/decisions on supply chain performance. Traditional analysis methods struggle in this environment of large, complex datasets with hundreds of features becoming the norm in supply chains. We present an empirical analysis framework termed Scenario Trees that provides a novel representation for impulse and delayed scenario events and a direction for modeling multivariate constrained responses. Amongst potential learners, supervised learners and feature extraction strategies based on tree-based ensembles are employed to extract the most impactful scenarios and predict their outcome on metrics at different product hierarchies. These models are able to provide accurate predictions in modeling environments characterized by incomplete datasets due to product substitution, missing values, outliers, redundant features, mixed variables and nonlinear interaction effects. Graphical model summaries are generated to aid model understanding. Models in complex environments benefit from feature selection methods that extract non-redundant feature subsets from the data. Additional model simplification can be achieved by extracting specific levels/values that contribute to variable importance. We propose and evaluate new analytical methods to address this problem of feature value selection and study their comparative performance using simulated datasets. We show that supply chain surveillance can be structured as a feature value selection problem. For situations such as new product introduction, a bottom-up approach to scenario analysis is designed using an agent-based simulation and data mining framework. This simulation engine envelopes utility theory, discrete choice models and diffusion theory and acts as a test bed for enacting different business scenarios. We demonstrate the use of machine learning algorithms to analyze scenarios and generate graphical summaries to aid decision making.
ContributorsShinde, Amit (Author) / Runger, George C. (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Janakiram, Mani (Committee member) / Arizona State University (Publisher)
Created2012
Description
The complexity of supply chains (SC) has grown rapidly in recent years, resulting in an increased difficulty to evaluate and visualize performance. Consequently, analytical approaches to evaluate SC performance in near real time relative to targets and plans are important to detect and react to deviations in order to prevent major disruptions.
Manufacturing anomalies, inaccurate forecasts, and other problems can lead to SC disruptions. Traditional monitoring methods are not sufficient in this respect, because com- plex SCs feature changes in manufacturing tasks (dynamic complexity) and carry a large number of stock keeping units (detail complexity). Problems are easily confounded with normal system variations.
Motivated by these real challenges faced by modern SC, new surveillance solutions are proposed to detect system deviations that could lead to disruptions in a complex SC. To address supply-side deviations, the fitness of different statistics that can be extracted from the enterprise resource planning system is evaluated. A monitoring strategy is first proposed for SCs featuring high levels of dynamic complexity. This presents an opportunity for monitoring methods to be applied in a new, rich domain of SC management. Then a monitoring strategy, called Heat Map Contrasts (HMC), which converts monitoring into a series of classification problems, is used to monitor SCs with both high levels of dynamic and detail complexities. Data from a semiconductor SC simulator are used to compare the methods with other alternatives under various failure cases, and the results illustrate the viability of our methods.
To address demand-side deviations, a new method of quantifying forecast uncer- tainties using the progression of forecast updates is presented. It is illustrated that a rich amount of information is available in rolling horizon forecasts. Two proactive indicators of future forecast errors are extracted from the forecast stream. This quantitative method re- quires no knowledge of the forecasting model itself and has shown promising results when applied to two datasets consisting of real forecast updates.
Manufacturing anomalies, inaccurate forecasts, and other problems can lead to SC disruptions. Traditional monitoring methods are not sufficient in this respect, because com- plex SCs feature changes in manufacturing tasks (dynamic complexity) and carry a large number of stock keeping units (detail complexity). Problems are easily confounded with normal system variations.
Motivated by these real challenges faced by modern SC, new surveillance solutions are proposed to detect system deviations that could lead to disruptions in a complex SC. To address supply-side deviations, the fitness of different statistics that can be extracted from the enterprise resource planning system is evaluated. A monitoring strategy is first proposed for SCs featuring high levels of dynamic complexity. This presents an opportunity for monitoring methods to be applied in a new, rich domain of SC management. Then a monitoring strategy, called Heat Map Contrasts (HMC), which converts monitoring into a series of classification problems, is used to monitor SCs with both high levels of dynamic and detail complexities. Data from a semiconductor SC simulator are used to compare the methods with other alternatives under various failure cases, and the results illustrate the viability of our methods.
To address demand-side deviations, a new method of quantifying forecast uncer- tainties using the progression of forecast updates is presented. It is illustrated that a rich amount of information is available in rolling horizon forecasts. Two proactive indicators of future forecast errors are extracted from the forecast stream. This quantitative method re- quires no knowledge of the forecasting model itself and has shown promising results when applied to two datasets consisting of real forecast updates.
ContributorsLiu, Lei (Author) / Runger, George C. (Thesis advisor) / Gel, Esma (Committee member) / Pan, Rong (Committee member) / Janakiram, Mani (Committee member) / Arizona State University (Publisher)
Created2015
Description
Despite significant growth in research about supply chain integration, many questions remain unanswered regarding the path to integration and the benefits that can be accrued. This dissertation examines three aspects of supply chain integration in the health sector, leveraging the healthcare context to extend the theoretical boundaries, as well as applying supply chain knowledge to an industry known to be immature in terms of its supply chain practices.
In the first chapter, a supply chain operating model that breaks away from the traditional healthcare supply chain structures is examined. Consolidated Service Centers (CSCs) embody a shared services strategy, consolidating supply chain functions across multiple hospitals (i.e. horizontal integration) and disintermediating several key roles in healthcare supply chains such as the group purchasing organizations and national distributors. Through case studies, key characteristics of CSCs that enable them to reduce the level of supply chain complexity are examined.
The second chapter investigates buyer-supplier relationships in healthcare (i.e. supplier integration), where a high level of distrust exists between hospitals and their suppliers. This context is leveraged to study both enablers and barriers to buyer-supplier trust. The results suggest that contracting counteracts the negative effects of dependence on trust. Furthermore, the study reveals that hospital buyers may, in some situations, perceive dedicated resource investments made by suppliers as trust barriers, associating such investments with supplier upselling and entrenchment tactics. This runs contrary to how dedicated investments are perceived in most other industries.
In the third chapter, the triadic relationship between the hospital, supplier, and physician is taken into consideration. Given their professional autonomy and power, physicians commonly undermine hospital efforts in supply base rationalization and standardization. This study examines whether physician-hospital integration (i.e. customer integration) can drive physicians towards supply selection practices that align with the hospital’s sourcing strategies and ultimately result in better supply chain performance. This study utilizes theory on agency triads and professionalism and tests hypotheses through a random effects regression model applied to data about hospital financial performance and physician-hospital arrangements.
In the first chapter, a supply chain operating model that breaks away from the traditional healthcare supply chain structures is examined. Consolidated Service Centers (CSCs) embody a shared services strategy, consolidating supply chain functions across multiple hospitals (i.e. horizontal integration) and disintermediating several key roles in healthcare supply chains such as the group purchasing organizations and national distributors. Through case studies, key characteristics of CSCs that enable them to reduce the level of supply chain complexity are examined.
The second chapter investigates buyer-supplier relationships in healthcare (i.e. supplier integration), where a high level of distrust exists between hospitals and their suppliers. This context is leveraged to study both enablers and barriers to buyer-supplier trust. The results suggest that contracting counteracts the negative effects of dependence on trust. Furthermore, the study reveals that hospital buyers may, in some situations, perceive dedicated resource investments made by suppliers as trust barriers, associating such investments with supplier upselling and entrenchment tactics. This runs contrary to how dedicated investments are perceived in most other industries.
In the third chapter, the triadic relationship between the hospital, supplier, and physician is taken into consideration. Given their professional autonomy and power, physicians commonly undermine hospital efforts in supply base rationalization and standardization. This study examines whether physician-hospital integration (i.e. customer integration) can drive physicians towards supply selection practices that align with the hospital’s sourcing strategies and ultimately result in better supply chain performance. This study utilizes theory on agency triads and professionalism and tests hypotheses through a random effects regression model applied to data about hospital financial performance and physician-hospital arrangements.
ContributorsAbdulsalam, Yousef J (Author) / Schneller, Eugene S (Thesis advisor) / Gopalakrishnan, Mohan (Committee member) / Maltz, Arnold (Committee member) / Dooley, Kevin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Firms are increasingly being held accountable for the unsustainable actions of their suppliers. Stakeholders, regulatory agencies, and customers alike are calling for increased levels of transparency and higher standards of corporate social responsibility (CSR) performance for suppliers. While it is apparent that supplier performance is important, it remains unclear how the stock market weighs the CSR performance of a supplier relative to that of a focal firm. This dissertation focuses on whether these relative differences exist. In addition to capturing the magnitude of the difference in market impact between focal firm and supplier CSR events; I analyze the ways in which these differences have changed over time. To capture this evolution, CSR events ranging over a period from 1994 to 2013 are examined. This research utilizes an event study methodology in which the announcement of over 2,300 CSR events are identified and analyzed to determine the subsequent stock market reaction. I find that while the market evaluated negative supplier CSR events less harshly than events occurring at the buying firm in the early years of the sample, by the turn of the millennium this “supplier discounting" had disappeared. The analysis is broken down by CSR event "type". Findings demonstrate that negative CSR events, particularly those revolving around worker or customer safety, generate the most significant abnormal return. The findings of this dissertation produce valuable managerial insights along with interpretation. Resources are scarce, and understanding where a firm might best allocate their resources to avoid financial penalties will be valuable information for corporate decision makers. These findings present clear evidence that some of these resources should be allocated to supplier CSR performance, not just towards the CSR performance of the focal firm.
ContributorsRogers, Zachary S (Author) / Carter, Craig (Thesis advisor) / Dooley, Kevin (Committee member) / Singhal, Vinod (Committee member) / Arizona State University (Publisher)
Created2016
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