Measuring the Welfare Effects of Shame and Pride

Public recognition is frequently used to motivate desirable behavior, yet its welfare effects—such as costs of shame or gains from pride— are rarely measured. We develop a portable empirical methodology for measuring and monetizing social image utility, and we deploy it in experiments on exercise and charitable behavior. In all experiments, public recognition motivates desirable behavior but creates highly unequal image payoffs. High-performing individuals enjoy significant utility gains, while low-performing individuals incur significant utility losses. We estimate structural models of social signaling, and we use the models to explore the social efficiency of public recognition policies. (JEL C93, D64, D82, D91)

GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things

With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.

Consumer Acceptance and Resistance Factors Toward Smart Retail Stores

The internet of things (IoT) allows companies to better understand consumers' needs while improving sales conditions (e.g., easier access to products and information, gain of time for employees and consumers, smart entertaining environments, etc.). However, digitalizing a store is an investment. Therefore, it is necessary for managers to know consumers' expectations and the perceived benefits and risks for both managers and consumers. This chapter studies the academic literature and managers opinions about consumers' attitudes toward the acceptance of smart retail stores. More specifically, the roles of consumer well-being, social image, utility value, human value, privacy concerns, technology trust, health concerns, and different personality traits toward smart retail stores are discussed.