Search

Consumers are exposed daily to various subtle marketing stimuli such as colors, brand logos, products characterized by multiple attributes or advertising messages bombarding them from numerous channels. All these subtle marketing cues can influence consumer judgment and decision-making, very often without their awareness. This dissertation demonstrates how states of varying motivational intensity (active vs. completed goals; unfulfilled vs. fulfilled desires) affect how consumers respond to such subtle marketing cues. Some examples of such cues specifically explored in this dissertation are primes (subtle cues incidentally activating knowledge structures such as trait concepts and stereotypes; Bargh, Chen, and Burrows 1996) or assortment cues. These subtle marketing stimuli are often embedded in the context, but are at the same time inconspicuous and unobtrusive, mildly steering consumers into specific decisions and choices. Through a specific focus on states of varying motivational intensity this dissertation pinpoints when and how subtle marketing cues are most likely to influence consumer judgment, decision-making, and behavior. As such, it presents a more refined picture looking at processing of contextual information through the lens of currently active motivations. Therefore, the main contribution of this work is to contextualize previous findings, demonstrating not only when subtle contextual cues (e.g, primes, assortment cues) drive consumer decision-making, but also when they fail to shape these decisions.

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS are necessary for efficient workspaces to be developed. In this paper, we examine two memoryless positioning techniques, K-Nearest Neighbor (KNN), and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: Zigbee, Bluetooth Low Energy (BLE), and WiFi (2.4 GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The RSSI dataset of all the experiments is available online.

Technological advances in the Internet of Things (IoT) have lead the way for technology to be used in ways that were never possible before. Through the development of devices with low-power radios, Wireless Sensor Networks (WSN) can be configured for almost any type of application. Agricultural has been one example where IoT and WSN have been able to increase productivity, efficiency, and output yield. Systems that previously required manual operation can be easily replaced with sensors and actuators to automate the process such as irrigation and disease management. Powering these devices is a concern as batteries are often required due to devices being located where electricity is not readily available. In this paper, a comparison is performed between three wireless technologies: IEEE 802.15.4 (Zigbee), Long Range Wireless Area Network (LoRaWAN), and IEEE 802.11g (WiFi 2.4~GHz) for agricultural monitoring with energy harvesting capabilities. According to experimental results, LoRaWAN is the optimal technology to use in an agricultural monitoring system where power consumption and network lifetime are a priority. The experimental results can be used for the selection of wireless technology for agricultural monitoring following application requirements.

Consumers are exposed daily to various subtle marketing stimuli such as colors, brand logos, products characterized by multiple attributes or advertising messages bombarding them from numerous channels. All these subtle marketing cues can influence consumer judgment and decision-making, very often without their awareness. This dissertation demonstrates how states of varying motivational intensity (active vs. completed goals; unfulfilled vs. fulfilled desires) affect how consumers respond to such subtle marketing cues. Some examples of such cues specifically explored in this dissertation are primes (subtle cues incidentally activating knowledge structures such as trait concepts and stereotypes; Bargh, Chen, and Burrows 1996) or assortment cues. These subtle marketing stimuli are often embedded in the context, but are at the same time inconspicuous and unobtrusive, mildly steering consumers into specific decisions and choices. Through a specific focus on states of varying motivational intensity this dissertation pinpoints when and how subtle marketing cues are most likely to influence consumer judgment, decision-making, and behavior. As such, it presents a more refined picture looking at processing of contextual information through the lens of currently active motivations. Therefore, the main contribution of this work is to contextualize previous findings, demonstrating not only when subtle contextual cues (e.g, primes, assortment cues) drive consumer decision-making, but also when they fail to shape these decisions.