A Novel Approach To Measure Product Quality In Sustainable Supplier Selection

A gap remains due to the intangible and qualitative criteria used to measure product quality for supplier evaluation and selection. Improving product quality is a crucial strategy for achieving reduce, reuse, recycle, and recovery. Quality characteristics are described as functional relationships (called profiles), and with the advancements in measurement technology, high dimensional data are collected. Nonetheless, prior studies have not addressed sustainable supplier selection where a nonlinear profile characterizes the product quality. Hence, this study aims to provide a novel approach to measure product quality using the process yield index, presents multiple comparisons with the best and difference test statistics and proposes a Bonferroni correction method. This study applies a Monte Carlo simulation to find the selection power and the required number of profiles. The statistical properties are investigated, and a comparison study is performed. The results show that multiple comparisons with the best outperform the Bonferroni method regarding the sample size requirement and power, and the number of levels and profiles were found to impact the power of the statistical tests. The required number of profiles and the critical value are tabulated for decision-makers

The Influence of media displays and image quality attributes for HDR image reproductions

High Dynamic Range (HDR) photography has been in existence at least since the time of Ansel Adams, with his experiments using analog film and darkroom techniques for the production of black and white prints in the 1940\u27s (Ashbrook, 2010). This photographic method has the ability to provide a more accurate representation of a scene through a greater range of the light and dark areas captured in an image. In the mid-20th century HDR Photography it has continued to grow in popularity among those interested in photography wishing to optimize their resulting image beyond a more commonly used technique. Presently, the limitations of commonly available reproduction technologies can lead to unpredictable output results through media such as monitor displays and inkjet prints. The purpose of this research was to determine the influence of quality attributes and image content on the preference of display media for HDR image reproductions. To achieve this purpose, a psychophysical experiment was conducted of 38 observers with previous imaging related exposure. This part of the study consisted of HDR comparisons across both a monitor display device and inkjet prints. Through qualitative and quantitative methods, common trends were identified among observer responses. The results show that for inkjet prints are the most preferred for the output of HDR images, specifically when printed on a metallic substrate. Additionally, the content of displayed images can directly impact display preference depending on the viewer\u27s perception and relationship formed with the photographic image. When evaluating HDR images across two media platforms, quality attributes comprising of a strong influence towards preference are sharpness, naturalness, contrast and highlights while artifacts, physical qualities and shadows were found to have barely any influence. Within the attributes related to HDR, relationships between attributes are found to be significant regarding image evaluation, leading to areas of further research

Essays On Referral Programs And Preference Estimation

In this dissertation, we study referral programs and preference estimation in two essays. In the first essay, we propose that a firm can enhance the effectiveness of its referral program by promoting better matching between referred customers and the firm. We develop three treatments aimed at promoting better matching, including (1) offering current customers a gift before inviting them to refer friends, (2) notifying current customers about the value that they have received from the firm before inviting them to refer friends, and (3) rewarding referring customers based on the value of their referred customers. We test these three treatments by conducting two field experiments in collaboration with a Chinese online financial services firm. We find that all three treatments substantially enhanced the effectiveness of the focal referral program, measured for each current customer as the total value of his referred customers. We also find that the enhancement was primarily driven by the acquisition of higher-value new customers rather than the acquisition of more new customers. In addition, we investigate customer heterogeneity in treatment effects and explore the mechanisms through which these treatments impacted customer referrals. In the second essay, we develop a new model for effective modeling of consumer heterogeneity in choice-based conjoint estimation. Assuming that most variations in consumers\u27 partworth vectors are along a small number of orthogonal directions, we propose that shrinking the individual-level partworth vectors toward a low-dimensional affine subspace that is also inferred from data can be an effective approach to pooling information across consumers and modeling consumer heterogeneity. We develop a low-dimension learning model to implement this information pooling mechanism that builds on recent advances in rank minimization and machine learning. We evaluate the empirical performance of the low-dimension learning model using both simulation experiments and field choice-based conjoint data sets. We find that the low-dimension learning model overall outperforms multiple benchmark models in terms of both parameter recovery and predictive accuracy. While addressing two different marketing topics, both essays share a common theme - careful modeling of consumer heterogeneity plays a key role in understanding consumer behavior and developing effective marketing strategies

Essays In Matching Markets

I present two experiments exploring failures in matching markets. In the first experiment, I introduce a new experimental paradigm to evaluate employer preferences, called Incentivized Resume Rating (IRR). Employers evaluate resumes they know to be hypothetical in order to be matched with real job seekers, preserving incentives while avoiding the deception necessary in audit studies. I deploy IRR with employers recruiting college seniors from a prestigious school, randomizing human capital characteristics and demographics of hypothetical candidates. I measure both employer preferences for candidates and employer beliefs about the likelihood candidates will accept job offers, avoiding a typical confound in audit studies. I discuss the costs, benefits, and future applications of this new methodology. In the second experiment, I examine out-of-equilibrium truth-telling in strategic matching markets. In two-sided settings, market designers tend to advocate for deferred acceptance (DA) over priority mechanisms, even though theory tells us that both types of mechanisms can yield unstable matches in incomplete information equilibrium. However, if match participants on the proposed-to side deviate from equilibrium by truth-telling, then DA yields stable outcomes. In a novel experimental setting, I find out-of-equilibrium truth-telling under DA but not under a priority mechanism, which could help to explain the success of DA in preventing unraveling in the field. I then attempt to explain the difference in behavior across mechanisms by estimating an experience-weighted learning model adapted to this complex strategic environment. I find that initial cognition and willingness to explore new strategies drive the difference in agents\u27 ability to find strategic equilibria

Resource Allocation Contests: Experimental Evidence

Across many forms of rent seeking contests, the impact of risk aversion on equilibrium play is indeterminate. We design an experiment to compare individuals’ decisions across three contests which are isomorphic under risk-neutrality, but are typically not isomorphic under other risk preferences. The pattern of individual play across our contests is not consistent with a Bayes-Nash equilibrium for any distribution of risk preferences. We show that replacing the Bayes-Nash equilibrium concept with the quantal response equilibrium, along with heterogeneous risk preferences can produce equilibrium patterns of play that are very similar to the patterns we observe.rent seeking, experiments, risk aversion, game theory

Intertemporal utility and correlation aversion

Convenient assumptions about qualitative properties of the intertemporal utility function have generated counter-intuitive implications for the relationship between atemporal risk aversionand the intertemporal elasticity of substitution. If the intertemporal utility function is additively separable then the latter two concepts are the inverse of each other. We review a simple theoretical specification with a long lineage in the literature on multi-attribute utility, and demonstrate the critical role of a concept known as intertemporal risk aversion or intertemporal correlation aversion.This concept is the intertemporal analogue of a more general concept applied to two attributes of utility, but where the attributes just happen to be the time-dating of the good. In the context of intertemporal utility functions, the concept provides an intuitive explanation of possible differences between (the inverse of) atemporal risk aversion and the intertemporal elasticity of substitution. We use this theoretical structure to guide the design of a series of experiments that allow us to identify and estimate intertemporal correlation aversion. Our results show that subjects are correlation averse over lotteries with intertemporal income profiles, and that the convenient additive specification of the intertemporal utility function is not an appropriate representation of preferences over time