Assessing the effect of mobile word-of-mouth on consumers : the physical, psychological and social influences

Mobile technologies enable users to discover and research products anytime, anywhere. Mobile devices allow consumers to create and share content based on physical location, facilitate seamless interactions, and provide context-relevant information that can better satisfy users’ needs and enhance their shopping experience. As consumers increasingly rely on mobile devices to search information and purchase products, they need immediate, updated, informative and credible opinions in concise forms. Meanwhile, marketers face unprecedented opportunities for mobile marketing, making ever important for them to understand the mobile word-of-mouth and its effect on the purchase behaviors of consumers on the mobile platform vs. those on other devices. Drawing from the media richness theory and the principle of compensatory adaptation, study one performs sentiment analysis of online product reviews from both mobile and desktop devices by analyzing over one million customer reviews from Dianping.com. We find that mobile reviews are naturally shorter, contain more adverbs and adjectives, and have smaller readership and less votes of helpfulness. The product ratings from mobile reviews are more polarized yet the average valence of mobile reviews is higher. By comparison, desktop reviews contain more pictures and are rated more helpful. Lastly, pricy products receive more desktop reviews than mobile ones. Study two draws from the construal level theory and posit that WOM from mobile devices reflects closer psychological distances (temporal and social), thus constitutes a lower construal level than that from desktop computers. Using a dataset of over one million product reviews from Dianping.com, we assess the value of online product reviews from mobile devices in comparison with those from the desktop computers. Our findings show that WOM is more helpful when it is socially and temporally closer to the users and this effect is amplified when using mobile devices, which bring the mental construal to a low level and make others’ opinions more relevant. Further, we show that product type moderates the effect of online reviews in that m-WOM is more influential for hedonic products and its value for the utilitarian consumption is the lowest. Study three deploys the observational learning theory to examine the effect of WOM across the mobile and desktop devices on the purchase behavior of online promotional offers. The findings suggest that the effect of WOM on the purchase of promotion offers varies significantly across the platforms, product categories, and discount rates. These findings help better understand the strengths, limitations and the effect of m-WOM as marketers attempt to offer consumers context-sensitive and time-critical promotions through mobile devices and make a significant contribution to the literature on interactive marketing. These studies render meaningful implications for theory development about the role of mobile technologies in marketing and can assist practitioners formulating effective promotional strategies through the electronic channels via mobile and desktop devices

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way