Investigating the role of regulatory focus in apparel attribute prioritization

PurposeThe purpose of this study is to utilize consumers' regulatory focus as a segmentation variable to understand how and why consumers shift their tendency to prioritize certain apparel attributes.Design/methodology/approachSix hypotheses are developed and then tested via two experiments. Self-administered online questionnaire is used to collect data from a total of 1,178 participants recruited via Amazon Mechanical Turk. The collected data is analyzed using series of Chi-square tests and ANOVAs.FindingsResults show that promotion-focused (prevention-focused)) consumers are not only more likely to prioritize apparel attributes that ensure the attainment of satisfaction (avoidance of dissatisfaction) but also attach higher monetary value to apparel products bearing such attributes.Originality/valuePrevious studies of apparel attribute prioritization utilized static segmentation variables such as age or gender despite the dynamic nature of attribute prioritization tendency. This study extends the literature by demonstrating the significance of consumers' regulatory focus – a dynamic segmentation variable that has not been studied in the current context.

Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor

Data segmentation is an essential process in activity recognition when using machine learning techniques. Previous studies on physical activity recognition have mostly relied on the sliding window approach for segmentation. However, choosing a fixed window size for multiple activities with different durations may affect recognition accuracy, especially when the activities belong to the same category (i.e., dynamic or static). This paper presents and verifies a new method for dynamic segmentation of physical activities performed during the rehabilitation of individuals with spinal cord injuries. To adaptively segment the raw data, signal characteristics are analyzed to determine the suitable type of boundaries. Then, the algorithm identifies the time boundaries to represent the start- and endpoints of each activity. To verify the method and build a predictive model, an experiment was conducted in which data were collected using a single wrist-worn accelerometer sensor. The experimental results were compared with the sliding window approach, indicating that the proposed method outperformed the sliding window approach in terms of overall accuracy, which exceeded 5%, as well as model robustness. The results also demonstrated efficient physical activity segmentation using the proposed method, resulting in high classification performance for all activities considered.

Machine Learning-Based Cooperative Spectrum Sensing in Dynamic Segmentation Enabled Cognitive Radio Vehicular Network

A vehicle ad hoc network (VANET) is a solution for road safety, congestion management, and infotainment services. Integration of cognitive radio (CR), known as CR-VANET, is needed to solve the spectrum scarcity problems of VANET. Several research efforts have addressed the concerns of CR-VANET. However, more reliable, robust, and faster spectrum sensing is still a challenge. A novel segment-based CR-VANET (Seg-CR-VANET) architecture is therefore proposed in this paper. Roads are divided equally into segments, and they are sub-segmented based on the probability value. Individual vehicles or secondary users produce local sensing results by choosing an optimal spectrum sensing (SS) technique using a hybrid machine learning algorithm that includes fuzzy and naïve Bayes algorithms. We used dynamic threshold values for the sensing techniques. In this proposed cooperative SS, the segment spectrum agent (SSA) made the global decision using the tri-agent reinforcement learning (TA-RL) algorithm. Three environments (network, signal, and vehicle) are learned by this proposed algorithm to determine primary (licensed) users’ activities. The simulation results indicate that, compared to current works, the proposed Seg-CR-VANET produces better results in spectrum sensing.