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

Description: 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 tool. To see the impact of training, one group of the participants received the scenarios without training and the other group received them with training. The training consists of a brief description of the cognitive bias as well as an example of the cognitive bias. Cognitive reflection is operationalized using cognitive reflection test (CRT). The survey was distributed to students at University of North Texas (UNT). Logistic regression has been employed to analyze data. The research shows that participants show the cognitive biases proposed by Tversky and Kahneman. Moreover, CRT is significant factor to predict the cognitive bias in two ...
Date: May 2016
Creator: Alkhars, Mohammed

Decision-Making with Big Information: The Relationship between Decision Context, Stopping Rules, and Decision Performance

Description: Ubiquitous computing results in access to vast amounts of data, which is changing the way humans interact with each other, with computers, and with their environments. Information is literally at our fingertips with touchscreen technology, but it is not valuable until it is understood. As a result, selecting which information to use in a decision process is a challenge in the current information environment (Lu & Yuan, 2011). The purpose of this dissertation was to investigate how individual decision makers, in different decision contexts, determine when to stop collecting information given the availability of virtually unlimited information. Decision makers must make an ultimate decision, but also must make a decision that he or she has enough information to make the final decision (Browne, Pitts, & Wetherbe, 2007). In determining how much information to collect, researchers found that people engage in ‘satisficing' in order to make decisions, particularly when there is more information than it is possible to manage (Simon, 1957). A more recent elucidation of information use relies on the idea of stopping rules, identifying five common stopping rules information seekers use: mental list, representational stability, difference threshold, magnitude threshold, and single criterion (Browne et al., 2007). Prior research indicates a lack of understanding in the areas of information use (Prabha, Connaway, Olszewski, & Jenkins, 2007) and information overload (Eppler & Mengis, 2004) in Information Systems literature. Moreover, research indicates a lack of clarity in what information should be used in different decision contexts (Kowalczyk & Buxmann, 2014). The increase in the availability of information further complicates and necessitates research in this area. This dissertation seeks to fill these gaps in the literature by determining how information use changes across decision contexts and the relationships between stopping rules. Two unique methodologies were used to test the hypotheses in the conceptual ...
Date: August 2016
Creator: Gerhart, Natalie

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

Description: 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 of structural model. Monte Carlo simulation allows demonstrating the improvement of the proposed model fit indices in comparison to the established indices found within the SEM literature. These posited PLS methods, that are logical transfers of GLM methods, are tested using examples. These tests enable demonstrating the methods and recommending reporting requirements.
Date: August 2016
Creator: George, Benjamin Thomas

Factors that Drive New Technology Product and Service Use and Continuance

Description: Understanding information technology and its related products and services is increasingly important because the everyday use of technology continues to expand and broaden. Despite this need for greater understanding, the extant theories that explore the dominant factors that drive intention to use a new technology are limited. The Technology Acceptancy Model (TAM) is the most popular model in explaining traditional technology adoption. The limitations of the TAM in grasping the overall evaluation of technology or service are one of motivations for developing new models in this dissertation. The two antecedents of the TAM- perceived usefulness and perceived ease of use- only capture partial utility of a service (or product). In addition, some researchers argued that key factors used in an initial acceptance model such as perceived usefulness and perceived ease of use are not strong predictors of future continuance intention of the service because they do not consider future switching intention in the later stage. Hence, one goal of this dissertation is to develop and test new models to predict factors that drive intention and continuance intention decisions of new technology related products or services. This research involves three studies that examine different aspects of adoption and continuance intention decisions of new technology-related products or services. Essay 1 posits and empirically tests a new model that examines service vendor quality, service outcome quality, and trust as drivers of cloud-based services by adopting the frameworks from marketing, behavior intention and information technology research. The model is referred to as the quality trust model (QTM). The quality of cloud-based services involves the quality of vendors and the quality of service outcomes; its effectiveness is mirrored by trust of the services. Data from an online survey of 355 respondents were used to test the research model. The results show that vendor quality and ...
Date: May 2018
Creator: Liu, Xiaotong

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

Description: 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, the Deming Prize, and the European Foundation for Quality Management (EFQM) Award through analysis of the extensive literature on each as well as examination of the government documents about the frameworks. Comparisons show the Baldrige framework most widely serves as basic model for national quality award frameworks to increase the awareness of quality and promote the best QM practices. After reviewing the categories and their weightings in the frameworks of MBNQA, the Deming Prize, and the EFQM Award, we identify opportunities to refine the frameworks and promote QM theory development. Essay 2 fills a critical research gap by assessing the ...
Date: May 2016
Creator: Peng, Xianghui

Three Research Essays on Online Users' Concerns and Web Assurance Mechanisms

Description: Online users struggle with different concerns whenever they use information systems. According to Miyazaki and Fernandez (2001), there are three important categories of concerns for online users: privacy concern, third party fraudulent behavior concern ("system security"), and online website fraudulent behavior concern ("security"). Kim, Sivasailam, and Rao (2004) proposed a similar categorization for web assurance dimensions. They argue that online websites are supposed to address users' privacy, security, and business integrity concerns to decrease user concerns. Although several researchers tried to answer how different factors affect these concerns and how these concerns affect users' behavior, there are so many ambiguities and contradictions in this area. This Essay I in this work develops a comprehensive map of the role of online privacy concern to identify related factors and categorize them through an in-depth literature review and conducting meta-analysis on online privacy concern. Although users have concerns about their privacy and security, there is still growth in the number of internet users and electronic commerce market share. One possible reason is that websites are applying assurance mechanisms to ensure the privacy of their users. Therefore, it could be an interesting research topic to investigate how privacy assurance mechanisms affect users concern and, consequently, their behavior in different concerns such as e-commerce and social networking sites. Different types of web assurance mechanisms are used by websites. The most prevalent among these assurance mechanisms include web assurance seals and assurance statements and privacy customization features. Essay II and III aims to address how these mechanisms influence e-commerce and social networking sites users' behavior. Essay II applies the procedural fairness theory by Lind and Tyler (1988) to explain how and why the web assurance mechanisms affect consumers' perceived risks. Essay III addresses the issue of self-disclosure on social networking sites. Applying protection motivation theory, this ...
Date: August 2016
Creator: Mousavizadeh Kashipaz, Seyed Mohammadreza