Decision Makers’ Cognitive Biases in Operations Management: An Experimental Study (open access)

Decision Makers’ Cognitive Biases in Operations Management: An Experimental Study

Behavioral operations management (BOM) has gained popularity in the last two decades. The main theme in this new stream of research is to include the human behavior in Operations Management (OM) models to increase the effectiveness of such models. BOM is classified into 4 areas: cognitive psychology, social psychology, group dynamics and system dynamics (Bendoly et al. 2010). This dissertation will focus on the first class, namely cognitive psychology. Cognitive psychology is further classified into heuristics and biases. Tversky and Kahneman (1974) discussed 3 heuristics and 13 cognitive biases that usually face decision makers. This dissertation is going to study 6 cognitive biases under the representativeness heuristic. The model in this dissertation states that cognitive reflection of the individual (Frederick 2005) and training about cognitive biases in the form of warning (Kaufmann and Michel 2009) will help decisions’ makers make less biased decisions. The 6 cognitive biases investigated in this dissertation are insensitivity to prior probability, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity and misconception of regression. 6 scenarios in OM contexts have been used in this study. Each scenario corresponds to one cognitive bias. Experimental design has been used as the research …
Date: May 2016
Creator: AlKhars, Mohammed
System: The UNT Digital Library
Quality Management Theory Development and Investigation of the Constructs within an Organizational Framework (open access)

Quality Management Theory Development and Investigation of the Constructs within an Organizational Framework

Supply chain management (SCM) and quality management (QM) share some common literature and have overlapping domains that reinforce each other in the supplier and customer relationship management areas. Despite the recognized importance of supplier and customer relationships toward achieving quality goals, limited prior research examines whether SCM represents a distinct construct within the prominent existing quality focused organizational frameworks such as the Malcolm Baldrige National Quality Award (MBNQA). As a result of the absence of the SCM construct in the frameworks, the problem facing researchers is understanding the role of SCM in the implementation of QM practices within an organization. Such an understanding is key to QM theory development for the 21st century organizations. In order to conduct this investigation, we examine several well-studied quality focused organizational frameworks that are validated among the community of researchers, and, widely accepted among practitioners. However, which of these well-known quality management models serve as the best proxy for a quality focused organizational framework is an important area for research in order to better promote QM worldwide. This research involves three essays and uses a mixed methodology of qualitative and quantitative research. Essay 1 compares well-known national quality award frameworks such as the MBNQA, …
Date: May 2016
Creator: Peng, Xianghui
System: The UNT Digital Library
Extensions of the General Linear Model into Methods within Partial Least Squares Structural Equation Modeling (open access)

Extensions of the General Linear Model into Methods within Partial Least Squares Structural Equation Modeling

The current generation of structural equation modeling (SEM) is loosely split in two divergent groups - covariance-based and variance-based structural equation modeling. The relative newness of variance-based SEM has limited the development of techniques that extend its applicability to non-metric data. This study focuses upon the extension of general linear model techniques within the variance-based platform of partial least squares structural equation modeling (PLS-SEM). This modeling procedure receives it name through the iterative PLS‑SEM algorithm's estimates of the coefficients for the partial ordinary least squares regression models in both the measurement model and the overall structural model. This research addresses the following research questions: (1) What are the appropriate measures for data segmentation within PLS‑SEM? (2) What are the appropriate steps for the analysis of rank-ordered path coefficients within PLS‑SEM? and (3) What is an appropriate model selection index for PLS‑SEM? The limited type of data to which PLS-SEM is applicable suggests an opportunity to extend the method for use with different data and as a result a broader number of applications. This study develops and tests several methodologies that are prevalent in the general linear model (GLM). The proposed data segmentation approaches posited and tested through post hoc analysis …
Date: August 2016
Creator: George, Benjamin Thomas
System: The UNT Digital Library