The 2019 novel coronavirus is a non-segmented positive-sense RNA virus belonging to the Coronaviridae-Nidovirales family. We examined the swings in purchase behavior following the outbreak of the COVID-19 in Wuhan, China, and across the world based on the theory of fear appeal. We gathered published statistics (suspected, confirmed, and fatality) on the COVID-19 alongside the purchase of personal protective equipment to examine the swings in online purchase behavior. With a serial mediated analysis, we established that fear appeal is associated with the sharp dynamics in the online purchase as related to the COVID-19. The results confirmed that fear appeal promotes social presence in anticipation of seeking affection, acceptance, and social information. This feeling is a precondition for developing e-loyalty, which promotes purchase behavior. Even though our variables might not be conclusive enough, we believe the findings are fundamental to understanding the swings in the purchase trend in this and any similar situations.
Although the increasing mobile technology applies in our daily lives, there remain questions that clearly illustrate the experience inspired by the mobile technology environment. It is theoretically and practically meaningful to systematically reveal how mobile technology generates users' virtual experience and, subsequently, the behavioral response. To fill the gaps, drawing on the Stimulus-Organism-Response model, this paper regards flow as the core experience based on the features of the mobile users' experience. The framework is that mobile technology will promote users' virtual experience, and in turn, affect their behavioral response. This study uses online survey data from 452 respondents. We employ the structural equation model based on the partial least squares method and further mediation analysis by the Sobel test to validate the research hypotheses. Our results show that the mobile technology environment has a significantly positive effect on users' purchase intention/return mediated by the virtual experience. However, the direct relationship between ubiquity and flow, as well as telepresence and intention to purchase/return, are not significant. Finally, a few theoretical and managerial contributions are also discussed.
The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. The ability of Gaussian Naïve Bayes ML algorithm to predict stock price movement has not been addressed properly in the existing literature, hence this attempt to fill that gap in the literature by evaluating the performance of GNB algorithm when combined with different feature scaling and feature extraction techniques in stock price movement prediction. The performance of the GNB models set up were ranked using the Kendall's test of concordance for the various evaluation metrics used. The results indicated that, the predictive model based on integration of GNB algorithm and Linear Discriminant Analysis (GNB_LDA) outperformed all the other models of GNB considered in three of the four evaluation metrics (i.e., accuracy, and AUC). Similarly, the predictive model based on GNB algorithm, Min-Max scaling, and PCA produced the best rank using the specificity results. In addition, GNB produced better performance with Min-Max scaling technique than it does with standardization scaling techniques Povzetek: Predstavljena je metoda Gausovega naivnega Bayesa za borzne napovedi.
The current COVID-19 pandemic has led to a devastating socioeconomic predicament, which has resulted in the temporary closure and collapse of thousands of businesses across the globe. The quicker companies can respond to the current pandemic situation, the more likely their chances of surviving in the short or long term. Businesses around the world are compelled to make significant changes to their business operations, such as downsizing, product, and service diversification. To address these changes quickly, companies need to adopt or capitalize on their business intelligence strategies through agile risk management, artificial intelligence systems, and data analytics to help make informed decisions to enhance business operations amid COVID-19. This article outlines some practical and theoretical recommendations of business intelligence strategies for organizations and their service supply chain network on how to be adaptive, flexible, and innovative to survive and stay competitive during these challenging times by leveraging agile dimensions, artificial intelligence systems, and data analytics.
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