Embodied Location Effects: Affecting Consumer Product Attributions Through Location-concept Associations

A large number of marketing decisions (e.g., where to place products on a shelf; where to place information on product packages or within advertisements; how to organize product listings on online shopping sites) involve choices related to location. However, because particular locations can convey symbolic and conceptual meanings (e.g., “up” implies power), in order to choose the best location, marketers must understand what meaning is being communicated through a placement. Drawing on embodied cognition theory, which suggests that our thoughts, feelings, and behaviors are shaped through our interactions with the surrounding world and grounded in sensorimotor systems, this dissertation explores conceptual associations with various locations, identifying a new location-concept association (up and chronological newness) and providing insight into how marketers can utilize location effects to better promote product attributes and improve consumer well-being. Specifically, Essay 1 explores how marketers can use location-number associations to most effectively communicate nutrition information on food packages. Drawing on the number-location association literature (i.e., small numbers-left, large numbers-right), three experimental studies show that consumers estimate a higher nutrient content when nutrition claims are placed on the right (vs. left) side of the package, which has a subsequent impact on perceived healthfulness of the product. Also, Essay 1 examined the moderating role of product-nutrient associations and nutrient type (negative vs. positive). Essay 2 examines how marketers can use shelf location combined with a conceptual metaphor between verticality and power to increase consumers’ beliefs about green products’ effectiveness and consequent purchase. Findings from three experimental studies show that placing green products on a higher (vs. lower) shelf can improve perceived product effectiveness, which in turn increases purchase intention of the target product. Essay 2 also discusses the role of choice criteria (choosing strong and powerful products vs. mild and gentle products) as a moderator. Finally, Essay 3 identifies a hitherto unexplored conceptual association between up and chronological newness and demonstrates how marketers can utilize this association to better market products. Six studies find support for this association that consumers conceptually associate the chronological newness construct with up and that consumers use this association to infer newness-related information such as product novelty, newspaper credibility, and food freshness. Together, this dissertation contributes theoretically to the understanding of embodied cognition, particularly location-concept associations, in the marketing domain. Additionally, this dissertation provides managerial and public policy implications

Engineered biosynthesis of milbemycins in the avermectin high-producing strain Streptomyces avermitilis

Additional file 3 : Figure S2. HPLC analysis of milbemycins produced from S. avermitilis mutant strains and authentic standard milbemycins

Vulnerable maximizers: The role of decision difficulty

Adding to prior literature that has examined the relationship between maximization and dissatisfaction, the present research suggests that maximizers, as defined by the original maximization scale, are unhappier decision makers than satisficers because maximizers fail to adequately handle dissonant experiences. Throughout three studies that use different conceptualization and measurement of maximization, we show that maximizers are more vulnerable to negative feedback about one’s choice such that they decrease positivity toward the chosen option to a greater level than satisficers. However, this effect was mainly driven by the decision difficulty factor in the conceptualization of maximization. When decision difficulty was conceptualized as a defining component of maximization (Study 1 and 2), “maximizers” show greater positivity drop in the face of negative feedback. However, in the absence of a decision difficulty component, a recently proposed two-component model of maximization (the goal to get the best and search for alternatives; Cheek and Schwartz, 2016) did not play a significant role in predicting positivity drop, while perceived decision difficulty did (Study 3). Together our findings suggest that previously reported contradictory outcomes of maximization may be due to inconsistent conceptualization and measurement, especially treating decision difficulty as a defining component of maximization

RRED: A Radiology Report Error Detector based on Deep Learning Framework

Radiology report is an official record of radiologists interpretation of patients radiographs and its a crucial component in the overall medical diagnostic process. However, it can contain various types of errors that can lead to inadequate treatment or delay in diagnosis. To address this problem, we propose a deep learning framework to detect errors in radiology reports. Specifically, our method detects errors between findings and conclusion of chest X-ray reports based on a supervised learning framework. To compensate for the lack of data availability of radiology reports with errors, we develop an error generator to systematically create artificial errors in existing reports. In addition, we introduce a Medical Knowledge-enhancing Pre-training to further utilize the knowledge of abbreviations and key phrases frequently used in the medical domain. We believe that this is the first work to propose a deep learning framework for detecting errors in radiology reports based on a rich contextual and medical understanding. Validation on our radiologist-synthesized dataset, based on MIMIC-CXR, shows 0.80 and 0.95 of the area under precision-recall curve (AUPRC) and the area under the ROC curve (AUROC) respectively, indicating that our framework can effectively detect errors in the real-world radiology reports.N

MOESM5 of Engineered biosynthesis of milbemycins in the avermectin high-producing strain Streptomyces avermitilis

Additional file 5 : Figure S4. Sequence of inactivated aveD

MOESM3 of Engineered biosynthesis of milbemycins in the avermectin high-producing strain Streptomyces avermitilis

Additional file 3 : Figure S2. HPLC analysis of milbemycins produced from S. avermitilis mutant strains and authentic standard milbemycins