An Investigation of the Relationships between Goals and Software Project Escalation: Insights from Goal Setting and Goal Orientation Theories

Escalation of commitment is manifested as a behavior in which an individual resists withdrawing from a failing course of action despite negative feedback, and it is an enduring problem that occurs in a variety of situations, including R&D investment decisions and software project overruns. To date, a variety of theoretical explanations have been offered as to what causes escalation of commitment, including personal responsibility, the sunk cost effect, and the completion effect. Nonetheless, what is missing in our understanding is the role that goals can play in escalation situations. This represents a significant gap in escalation research, as goals are a fundamental element driving many human behaviors. Further, escalation researchers recently suggested that escalation behavior can be understood as an activity that is directed by goals. Therefore, this dissertation aims to generate insights regarding the impact of goals on escalation of commitment by drawing on goal setting and goal orientation theories. This dissertation consists of four essays each of which involves one or more studies

TREND: Truncated Generalized Normal Density Estimation of Inception Embeddings for GAN Evaluation

Evaluating image generation models such as generative adversarial networks (GANs) is a challenging problem. A common approach is to compare the distributions of the set of ground truth images and the set of generated test images. The Frech\'et Inception distance is one of the most widely used metrics for evaluation of GANs, which assumes that the features from a trained Inception model for a set of images follow a normal distribution. In this paper, we argue that this is an over-simplified assumption, which may lead to unreliable evaluation results, and more accurate density estimation can be achieved using a truncated generalized normal distribution. Based on this, we propose a novel metric for accurate evaluation of GANs, named TREND (TRuncated gEneralized Normal Density estimation of inception embeddings). We demonstrate that our approach significantly reduces errors of density estimation, which consequently eliminates the risk of faulty evaluation results. Furthermore, we show that the proposed metric significantly improves robustness of evaluation results against variation of the number of image samples.Comment: Accepted in ECCV 202