Optimal Information Filtering for Robust Aerocapture Trajectory Generation and Guidance

This chapter reviews the role of theory and dynamic systems theory for understanding common interaction techniques including: targetting, trajectory generation, panning, scrolling and zooming. It explains how can be seen to be at the foundations of Human–Computer Interaction and might be essential for making progress in novel forms of interface. It reinterprets Fitts’ classical work with theoretic tools. It also highlights the limitations of theory for design of human–computer loops.

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Who Likes What Kind of News? The Relationship Between Characteristics of Media Consumers and News Interest

SAGE Open ◽

10.1177/21582440211003089 ◽

2021 ◽

Vol 11 (1) ◽

pp. 215824402110030

Author(s):

Kai Kaspar ◽

Lisa Anna Marie Fuchs

Keyword(s):

Big Five ◽

Information Filtering ◽

Personal Characteristics ◽

Self Esteem ◽

Big Five Personality ◽

Regression Analyses ◽

Uses And Gratification ◽

German Speaking ◽

Explained Variance ◽

The Relationship

Stimulated by the uses-and-gratification approach, this study examined the joint relation of several consumer characteristics to news interest. In total, 1,546 German-speaking participants rated their interest in 15 major news categories and several personal characteristics, including gender, age, the Big Five personality traits, self-esteem, as well as general positive and negative affect. Regression analyses examined the amount of interindividual variance in news interest that can be explained by this set of consumer characteristics. Overall, the amount of explained variance differed remarkably across news categories, ranging from 4% for entertainment-related news to 25% for news about technology. The most powerful explaining variables were participants’ gender, age, openness to experiences, and their amount of general positive affect. The results suggest that news interest should be defined and operationalized as a concept with multiple facets covering a huge range of content. Also, the results are important for media producers and journalists with respect to the conflict between increased need gratification of consumers and information filtering via personalized news content.

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Information Acquisition and Its Incentives in an E-Commerce Supply Chain under the Offline Showroom Model

Journal of theoretical and applied electronic commerce research ◽

10.3390/jtaer16050100 ◽

2021 ◽

Vol 16 (5) ◽

pp. 1791-1804

Author(s):

Mengli Li ◽

Xumei Zhang

Keyword(s):

Supply Chain ◽

Information Acquisition ◽

Incentive Contracts ◽

Demand Forecast ◽

Competition Intensity ◽

Demand Information ◽

Optimal Information ◽

Acquisition Strategy ◽

The One ◽

The Impact

Recently, the showroom model has developed fast for allowing consumers to evaluate a product offline and then buy it online. This paper aims at exploring the optimal information acquisition strategy and its incentive contracts in an e-commerce supply chain with two competing e-tailers and an offline showroom. Based on signaling game theory, we build a mathematical model by considering the impact of experience service and competition intensity on consumers’ demand. We find that, on the one hand, information acquisition promotes supply chain members to obtain demand information directly or indirectly, which leads to forecast revenue. On the other hand, information acquisition promotes supply chain members to distort optimal decisions, which results in signal cost. The optimal information acquisition strategy depends on the joint impact of forecast revenue, signal cost and demand forecast cost. Notably, in some conditions, the offline showroom will not acquire demand information even when its cost is equal to zero. We also design two different information acquisition incentive contracts to obtain Pareto improvement for all supply chain members.

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