ABSTRACT
Purpose>Amidst the exponential spread of the COVID-19 pandemic, this study aims to explore the evolving dynamics underlying consumers' narratives about luxury-brands over social media. While visualizing these Online Luxury-Brand Self-Narratives (OLBSNs) as a decision-making situation, the authors question the “rational-being” assumption of the Net Valence Model (NVM) during a pandemic situation. Specifically, the authors draw upon Terror Management Theory (TMT) to explicate the role of pandemic-induced mortality salience in rendering the idealistic assumptions of NVM unattainable. The authors uncover evidence of risk-taking behavior among luxury consumers while using OLBSNs as a potential meaning-providing structure during the pandemic.Design/methodology/approach>This study employed a cross-sectional survey method. The authors conducted a structured Qualtrics survey to collect data from 588 respondents. The authors examined the hypothesized relationships using structural equation modeling.Findings>In contrast to the conventional wisdom of NVM, the results suggest a positive influence of not only perceived benefits but also perceived risks on intention to engage in OLBSN and brand advocacy during the ongoing pandemic.Research limitations/implications>This study explains the emerging dynamics of pandemic-induced mortality salience in OLBSN decision-making and has implications for luxury-brand marketers in designing brand communication strategies over social media.Originality/value>This study makes an original endeavor to extend NVM beyond rational decision-making context by integrating the theoretical tenets of TMT within NVM while also delineating the decision-making mechanism of OLBSNs during the pandemic.
ABSTRACT
The active global SARS-CoV-2 pandemic caused more than 167 million cases and 3.4 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process and despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on knowledge graphs, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently developed the Dr-COVID model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of candidate drugs, 32 of which currently being in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the Dr-COVID model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware—we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.