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ItemIntroduction to the Minitrack on Digital Methods( 2023-01-03)
ItemInvestigating Usefulness Configurations of Online Consumer Reviews: A Fuzzy-Set Qualitative Comparative Analysis( 2023-01-03)Online reviews have a significant impact on consumers’ purchasing decisions. Many researchers have studied the relationship between review usefulness based on various factors related to online review, but existing studies have focused only on the linear relationship between variables methodologically. Therefore, this study examines the usefulness of online reviews from a configurational perspective derived from the complex interactions between elements, and aims to identify how these configurations differ according to product types. This study developed a conceptual model by combining HSM and ELM based on the theoretical discussion on the information processing and analyzed 7,316 cases collected from Amazon.com using fsQCA. As a result, three configurations affecting online usefulness were derived from search goods and four from experience goods. In short, consumers consume reviews through the complex interaction of various factors related to reviews, and the factors affecting the usefulness of search goods and experience goods are different.
ItemMeasuring the Interference Effect of Bots in Disseminating Opposing Viewpoints Related to COVID-19 on Twitter Using Epidemiological Modeling( 2023-01-03)The activity of bots can influence the opinions and behavior of people, especially within the political landscape where hot-button issues are debated. To evaluate the bot presence among the propagation trends of opposing politically-charged viewpoints on Twitter, we collected a comprehensive set of hashtags related to COVID-19. We then applied both the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models to three different dataset states including, total tweets in a dataset, tweets by bots, and tweets by humans. Our results show the ability of both models to model the diffusion of opposing viewpoints on Twitter, with the SEIZ model outperforming the SIR. Additionally, although our results show that both models can model the diffusion of information spread by bots with some difficulty, the SEIZ model outperforms. Our analysis also reveals that the magnitude of the bot-induced diffusion of this type of information varies by subject.